363 ETL Jobs in India

You will lead Application Security and VAPT initiatives and own the full security testing lifecycle. You’ll also help the company’s applications meet the strict security standards required for defence industry certifications.

What You’ll Do

• Run end-to-end VAPT on web apps, APIs, and cloud environments, finding logic flaws, access control issues, and OWASP Top 10 vulnerabilities
• Set up and manage Semgrep in the CI/CD pipeline for static analysis, taint tracking, and secure code reviews
• Run automated and manual DAST scans with OWASP ZAP to simulate real-world attacks
• Validate security controls against defence and government compliance frameworks, and prepare technical evidence and PoCs for external audits
• Work with engineering teams to triage findings, guide fixes, and re-test before production releases

Job Title : AWS Data Engineer / Data Lake Engineer

Experience : 2 to 4 Years

Location : Gurugram Sector 43

Work Mode : 5 Days WFO

Employment Type : Full-Time

About the Role :

We are looking for a skilled AWS Data Engineer with 2 to 4 years of experience in designing, developing, and maintaining data pipelines and cloud-based data platforms.

The candidate will work on an enterprise AWS Data Lake platform that collects data from multiple business systems, processes large datasets, and makes them available for analytics, reporting, and application development.

The ideal candidate should have strong hands-on experience with AWS data services, Python, PySpark, SQL, and Apache Airflow, along with an understanding of data warehousing and API-based data serving.

Mandatory skills :

AWS Data Lake, S3, EMR, Glue, Athena, Redshift, Lambda, RDS, DMS, IAM, Python, PySpark, Apache Airflow, Advanced SQL, ETL / ELT Pipelines, Data Modeling, PostgreSQL, Production Support.

Key Responsibilities :

• Develop, maintain, and optimize data ingestion and ETL / ELT pipelines on AWS.
• Work with AWS services such as S3, Glue, EMR, Athena, Redshift, Lambda, RDS, and DMS.
• Build and manage data processing workflows using Python, PySpark, and Apache Airflow.
• Implement data lake architecture with staging, curated, and consumption layers.
• Write complex SQL queries and perform data transformation, validation, and modeling.
• Work with PostgreSQL and RDS for structured data storage and consumption.
• Support API-based data access using Hasura GraphQL or similar technologies.
• Monitor production data pipelines, troubleshoot failures, and perform root cause analysis.
• Optimize Spark jobs, SQL queries, and data pipelines for performance and cost efficiency.
• Follow AWS security practices, IAM permissions, and data governance standards.
• Collaborate with application, analytics, and business teams to understand data requirements.

Mandatory Skills :

• 2 to 4 years of experience in Data Engineering, AWS Data Engineering, or Data Platform Development.
• Strong hands-on experience with AWS services : S3, Glue, EMR, Athena, Redshift, Lambda, RDS, DMS, and IAM.
• Good experience in Python and PySpark.
• Practical experience with Apache Airflow for workflow orchestration.
• Strong SQL skills and understanding of database concepts.
• Experience in building and supporting ETL / ELT pipelines.
• Understanding of Data Lake architecture, data warehousing, and data modeling.
• Working knowledge of PostgreSQL or similar relational databases.
• Experience handling production pipeline issues and troubleshooting.

Good to Have :

• Experience with Hasura GraphQL or any GraphQL-based API / data-serving platform.
• Knowledge of DBT (Data Build Tool).
• Understanding of Lakehouse architecture and table formats such as Apache Iceberg or Delta Lake.
• Experience with Kafka or other streaming data technologies.
• Knowledge of Amazon Redshift performance optimization, distribution keys, and sort keys.
• Experience with AWS Aurora and API Gateway.
• BFSI, Banking, or Insurance domain experience.

Ideal Candidate Profile :

• Strong hands-on technical and problem-solving skills.
• Experience working on real-time or batch-based production data pipelines.
• Good understanding of data processing, transformation, and storage concepts.
• Ability to troubleshoot technical issues and work independently.
• Good communication and collaboration skills.

Note : Candidates with strong practical experience in AWS Data Lake, PySpark, Airflow, Python, and SQL will be preferred. Real-world project experience is more important than theoretical knowledge or certifications.

This is not an on-call support role. As our Integration Support Engineer, you will own Oddr's support function end to end, with full control over system changes and fixes and the independence to redesign processes and architecture once leadership approves your ideas. You will wear many hats: leader, integration engineer, automation builder, and customer partner. You will use Claude and other LLM tools to make support more agentic and automate repetitive work across Azure, DevOps, SQL, and cloud integrations.

The core responsibilities for the job include the following:

Own and Lead:

• Own the support board and be accountable for every support outcome.
• Decide on, build, test, and deploy fixes and changes to production.
• Propose process and architecture improvements; implement them independently once approved.
• Coordinate with engineering, product, and implementation to resolve issues.

Automate and Optimize:

• Build AI agents and workflows (Claude, other LLMs) for triage, log analysis, root-cause investigation, and customer replies.
• Replace repetitive manual work with scripts, scheduled jobs, and AI-assisted workflows.
• Turn team knowledge into reusable playbooks, prompts, and documentation.

Integrations, Cloud, and Data:

• Troubleshoot, build, and improve integrations using iPaaS, Azure services, REST/SOAP APIs, SFTP, JSON, and XML.
• Ship changes through Azure DevOps and SDLC practices.
• Write and optimize SQL for investigation, validation, and reconciliation.
• Shape cloud architecture for reliability, observability, and scale.

Customers:

• Work directly with customers as their technical partner, communicating clearly and proactively.
• Solve both technical and business problems, learning new systems as needed.
• Oddr: AI-powered billing, invoicing, and revenue intelligence for law firms.

Requirements:

• 5+ years in integration engineering or support, including leading or solely owning a support function.
• Proven ownership: driving changes independently and seeing improvements through.
• Hands-on use of LLM tools (Claude, ChatGPT, Copilot, or similar) to automate work.
• Scripting for automation (Python, PowerShell, or similar).
• Strong SQL and database troubleshooting skills.
• Experience with Azure (functions, logic apps, monitoring) and Azure DevOps or similar CI/CD.
• Solid grasp of cloud architecture, integration patterns, REST/SOAP APIs, and at least one iPaaS (e. g., Boomi).
• Curious, comfortable wearing multiple hats, and confident working directly with customers.
• Bachelor's degree in computer science, IT, or a related field.
• Able to work 6:00 PM - 5:00 AM IST.

Nice to Have:

• Experience building AI agents (MCP, tool-calling, LLM APIs).
• Azure, Boomi, or other cloud/iPaaS certifications.
• Experience with Intapp Integration Builder or legal-industry systems.

What Great Looks Like:

• You own the role: every issue has a clear owner, priority, and next step.
• Each quarter, more support work is handled by automation and AI.
• Root causes get fixed, not patched, and you deliver approved process and architecture improvements.
• Customers and internal teams see you as the go-to owner of support.

Lead Data Engineer – Databricks & Azure

• We are looking for a Lead Data Engineer with 5–7 years of experience in Azure, Databricks, and Microsoft Fabric to lead and mentor the data engineering team. The role involves implementing scalable data solutions using Databricks as the core platform, building modern data pipelines, and enabling seamless integration with Microsoft Fabric and Power BI.

Responsibilities & Requirements:

• Lead and mentor data engineers while driving hands-on technical delivery.
• Implement approved Databricks and Microsoft Fabric architectures and solutions.
• Develop metadata-driven bronze, silver, and gold data pipelines.
• Build reusable batch, incremental, CDC, and streaming ingestion pipelines.
• Strong hands-on experience with Azure Databricks, PySpark, SQL, and Delta Lake.
• Experience with Microsoft Fabric, OneLake, Direct Lake, and Power BI integration.
• Implement data quality checks, reconciliation, source-to-target mapping, and metadata management.
• Configure Unity Catalog for data governance, access control, auditing, and lineage.
• Implement CI/CD, automated deployments, and Datadog monitoring.
• Drive testing, documentation, operational readiness, and knowledge transfer.

Good to have:

• Experience with MDM and master data integration.
• Exposure to Azure DevOps or GitHub Actions.
• Experience with automated data testing and reconciliation frameworks.
• Experience working with onshore stakeholders and distributed engineering teams.
• Strong communication, problem-solving, and team leadership skills.

Job Overview:

We are seeking an experienced Cloud Data Engineer to the architecture, development, Data migration, ETL pipeline in cloud and optimization of a next-generation data platform. This is a development role for an individual with over 4 years of deep data engineering and data migration, data pipeline in AWS expertise, capable of driving technical direction, and delivering high-impact solutions in a fast-paced

4+ Years of hands-on, progressive experience in Cloud Data Engineering, cloud ecosystems AWS, Data Architecture

• Deep conceptual understanding of core data engineering principles, ETL/ELT patterns, and metadata management

•  Ability to translate complex business needs into actionable technical requirements.

•  Strong troubleshooting and problem-solving skills in distributed systems.

•  Agile/Scrum teamwork capabilities and version control practices

• Insurance industry experience preferred but not mandatory

• Tools:

o Cloud Environment: AWS (S3, IAM, VPC, Glue, Lambda etc.)

o Experience with dbt Core, Iceberg, AWS Glue Catalog, AWS lambda, Redshift, Athena, Splunk, AmazonQ, AWS streaming services, Git

o Strong SQL, Pyspark and Python

Lead Data Security Solution Engineer

Exp: 10+ years

Rate: 35 LPA

Location: Remote

Time zone: ET (US Eastern time)

Shift: Night Shift

Type: Fulltime​

Responsibilities:

1. Technical and Database Assessment

Review Data In-Licensing requirements, workflow maps, data flows, data lineage, entitlement rules, and dependency records.

Assess relational database platforms, schemas, connectivity methods, interfaces, drivers, service identities, query patterns, and downstream consumption.

Identify where licensed data is stored, transformed, replicated, extracted, cached, archived, or backed up.

Assess current database security capabilities and identify gaps, constraints, risks, and prerequisites.

2. Data-Level Authentication and Identity Integration

Assess authentication for named users, service identities, workloads, and analytical tools accessing databases.

Evaluate OAuth/OIDC or other identity- and token-based authentication directly to the database where supported by the selected technology.

Design integration between enterprise identities, security groups, project groups, database principals, roles, and policy engines.

Reduce reliance on shared or long-lived credentials where feasible and define secure credential or token handling for unavoidable exceptions.

Align with tools currently available and identify gaps if any,

3. Fine-Grained Authorization and Data Firewalling

Design database-level controls that restrict access to explicitly authorized users, groups, services, projects, or licensing populations.

Design and implement row-level security, column-level security, dynamic masking, views, policy-based filtering, or equivalent controls as appropriate.

Define isolation patterns for different licensed datasets and agreements while preserving permitted platform use for other users.

Ensure controls are consistently enforced as close to the data layer as technically feasible and are not solely dependent on application logic.

4. Auditing, Monitoring, and Traceability

Design database auditing to capture who accessed data, what was accessed, when access occurred, and which identity or service was used.

Define logging for queries, exports, failed access attempts, permission changes, and privileged activity where supported and required.

Integrate audit telemetry with appropriate monitoring and reporting capabilities and define retention and evidence requirements.

Validate that audit records are searchable, attributable, protected from inappropriate modification, and useful for investigation.

5. Data Lifecycle, Revocation, and Deletion

Design controls for access revocation when users change roles, leave a project, or when licensing rights expire.

Design methods to identify and remove licensed data from primary stores and relevant downstream copies, extracts, caches, or derived stores.

Address deletion implications for referential integrity, backups, archives, replication, recovery processes, and downstream consumers.

Define validation, exception handling, and evidence mechanisms to demonstrate that required deletion activities were completed.

6. Solution Architecture and Engineering

Translate requirements into current-state and target-state architectures, technical specifications, security patterns, and implementation plans.

Compare solution options and document trade-offs across security, feasibility, performance, scalability, operability, cost, and technology constraints.

Define database connectivity, policy-enforcement, identity, audit, monitoring, lifecycle, and integration components.

Collaborate with database, IAM, security architecture, infrastructure, application, data platform, governance, and scientific teams.

7. POC Design and Execution

Select suitable relational database and workflow candidates that represent Data In-Licensing scenarios and material technical risks.

Define POC architecture, test data, test identities, success criteria, negative tests, performance considerations, and rollback approach.

Implement and validate identity-based authentication, fine-grained authorization, data firewalling, auditing, access revocation, and deletion scenarios.

Document outcomes, limitations, operational considerations, reusable patterns, and recommendations for broader adoption.

What we expect:

Expected Deliverables

Technical assessment of databases, integrations, identities, and data flows

Current-state database security and capability assessment

Target-state data security architecture and detailed technical design

Database authentication and identity-integration design

Row-level, column-level, masking, and policy-enforcement design

Data firewalling and licensed-dataset isolation pattern

Audit, monitoring, traceability, and reporting design

Access revocation, retention, lifecycle, and deletion design

Solution options, feasibility, gap, dependency, and risk assessment

POC architecture, implementation, test results, and recommendations

Reusable implementation patterns and operational handover documentation

Required Technical Skills and Experience

Strong hands-on experience with relational databases, SQL, database architecture, schemas, roles, privileges, and access paths.

Practical experience implementing database-level authorization, including row-level and column-level controls or equivalent fine-grained mechanisms.

Strong knowledge of database authentication, IAM, OAuth 2.0, OIDC, tokens, service identities, workload identities, and secrets management.

Experience designing secure database connectivity and integrations for applications, analytical tools, APIs, ETL/ELT, and data pipelines.

Experience with database auditing, query/activity monitoring, centralized logging, alerting, and investigation support.

Understanding of data lineage, lifecycle, retention, revocation, deletion, backup, archive, and recovery implications.

Ability to evaluate heterogeneous database technologies and design platform-appropriate controls.

Strong architecture, engineering, troubleshooting, documentation, and stakeholder-collaboration skills.

Nice to have:

Experience with enterprise data platforms, cloud or hybrid databases, data catalogs, access governance, and policy-enforcement technologies.

Experience in pharmaceutical research, scientific data, intellectual property protection, data licensing, or another regulated environment.

Experience designing and executing security POCs and converting validated patterns into production-ready designs.

Knowledge of privacy, contractual-use restrictions, segregation, and evidence requirements affecting licensed data.

Job Title: Java + Apache Spark Developer

Experience: 5–10 Years

Location: Mumbai

Work Mode: Hybrid (3 Days WFO)

Job Description:

Looking for hands-on Java Developers with strong experience in Apache Spark and distributed data processing. Candidates should have experience building and optimizing data pipelines, working with Spark SQL, and using Snowflake or Databricks for enterprise data engineering solutions.

Mandatory Skills:

Java, Apache Spark, Spark SQL, Snowflake / Databricks, SQL, ETL.

SENIOR DATA ENGINEER – DATABRICKS & SNOWFLAKE

Experience: 7+ Years

Location: Remote

Employment Type: Full-Time

Budget: Up to ₹30 LPA

Time Zone: US Eastern Time (ET)

Shift: US ET Night Shift

Job Description

We are looking for an experienced Senior Data Engineer with strong expertise in Databricks, Snowflake, Apache Spark, and AWS to design, develop, and maintain scalable data engineering solutions.

The candidate will work on integrating data from multiple sources, building modern data pipelines, developing enterprise data models, and preparing data platforms for AI/ML applications.

Experience in healthcare, scientific data, or the histopathology domain will be an added advantage.

Key Responsibilities

• Design, build, and maintain scalable data pipelines to acquire, integrate, and manage data from diverse sources such as laboratory systems, MES, clinical supply, quality systems, and external partners.
• Develop and optimize data workflows for structured and unstructured data using Python, PySpark, R, SQL, Databricks, and Snowflake.
• Design and maintain enterprise-level data models and data repositories.
• Perform ETL development, data warehousing, database design, and data integration.
• Ensure data quality, versioning, traceability, and consistency to support AI/ML readiness.
• Collaborate with stakeholders and technical teams to translate business requirements into technical solutions.
• Manage multiple projects while ensuring scalability, performance, and reliability.

Required Skills

• 7+ years of experience in Data Engineering.
• Strong hands-on experience with Databricks and Snowflake.
• Excellent knowledge of Apache Spark / PySpark.
• Strong experience with AWS cloud services and AWS RDS.
• Experience with Oracle and Microsoft SQL Server (MSSQL).
• Proficiency in Python, R, and SQL.
• Strong understanding of ETL, Data Warehousing (DWH), data modeling, and database design.
• Experience with dbt and cloud-based data architectures.
• Knowledge of NoSQL and graph databases.
• Strong analytical, problem-solving, and stakeholder-management skills.
• Good English communication skills (B2 level or above).
• Willingness to work in the US Eastern Time (ET) night shift.

Preferred / Good to Have

• Experience in histopathology, healthcare, life sciences, or scientific data environments.
• Knowledge of CDISC, HL7, FHIR, OMOP, DICOM, or other regulated data standards.
• Experience handling high-dimensional data, including imaging and sensor data.
• Familiarity with MLOps and machine learning model deployment workflows.
• Knowledge of Manufacturing Execution Systems (MES) and Laboratory Information Management Systems (LIMS).
• Exposure to knowledge graphs and ontology-driven architectures.

Education

Bachelor's degree in Engineering, Data Science, Life Sciences, Computer Science, or a related field. A master's degree is preferred.

Interested candidates can apply with their updated resumes.

Key Skills

Advanced hands-on experience in Alteryx Designer.

Strong knowledge of Alteryx workflows, Macros (Standard, Batch & Iterative).

Excellent SQL skills, including complex queries, joins, CTEs and aggregations.

Strong experience in ETL/ELT, data transformation and integration.

Alteryx workflow performance optimization and troubleshooting.

Experience with Alteryx Server / Alteryx One administration.

Production support, workflow scheduling and deployment.

Knowledge of Snowflake, SQL Server and PostgreSQL.

Microsoft Entra ID and SCIM provisioning knowledge is preferred.

Excellent communication and stakeholder management skills.

Mandatory Requirements

Valid Alteryx Designer Core Certification or higher is mandatory.

Strong hands-on experience in Alteryx workflow development.

Practical experience with Alteryx Macros.

Strong SQL and ETL/ELT expertise.

Experience in production support and performance optimization.

Excellent communication skills.

Good-to-Have Skills

Private Equity / Investment Management domain experience.

Alteryx Designer Advanced or Expert Certification.

Python (Pandas), Snowflake and REST APIs.

Microsoft Entra ID and SCIM 2.0.

Git, CI/CD and workflow automation.

Key Responsibilities

Design, develop and maintain enterprise-grade Alteryx workflows.

Build reusable macros and optimize complex ETL/ELT processes.

Manage workflow deployment, scheduling and monitoring.

Perform performance tuning and troubleshoot production issues.

Integrate multiple databases, APIs and enterprise data sources.

Ensure data accuracy, reconciliation and operational reliability.

Coordinate with business and technical stakeholders.

Navtech is a premier IT software and services provider. Our mission is to increase public cloud adoption and build cloud-first solutions that become trendsetting platforms of the future.

We have been recognized as the Best Cloud Service Provider by GoodFirms for delivering quality services and results. We strive to innovate and push technology and service boundaries to provide best-in-class technology solutions to our clients globally.

We are a fast-growing company serving clients across the United States, UK and Europe. You will work with a team of talented developers, quality engineers and product managers who are passionate about building impactful technology solutions.

About the Role

We are looking for an experienced Senior SQL Server / SSIS Engineer to design, develop, maintain and optimise database solutions and ETL workflows.

The ideal candidate should have strong hands-on experience with Microsoft SQL Server, SSIS package development, T-SQL programming, database performance tuning and data migration.

You should be comfortable working with existing database systems, troubleshooting complex issues, improving performance and ensuring data integrity, security and availability.

Key Responsibilities

• Design, develop and maintain SQL Server databases, database objects and logical data models.
• Develop, deploy and maintain SSIS packages for ETL processes and data integration.
• Build and optimise complex SQL queries, stored procedures, functions, views and triggers.
• Design and implement data extraction, transformation and loading workflows across multiple systems.
• Perform database migrations, data transformations and validation activities.
• Monitor and troubleshoot SQL Server databases, SSIS packages and SQL Server Agent jobs.
• Identify and resolve performance bottlenecks through query optimisation, indexing and execution plan analysis.
• Handle database administration activities, including upgrades, backups, recovery and maintenance.
• Ensure database performance, security, availability and data integrity.
• Troubleshoot database locking, blocking and resource utilisation issues.
• Collaborate with developers, QA engineers and other stakeholders to understand database requirements.
• Maintain technical documentation for database structures, ETL workflows and processes.
• Provide production support and proactively identify opportunities to improve database performance and reliability.

Who Are We Looking For?

• 6–10 years of experience in SQL Server development and database engineering.
• Strong hands-on experience with Microsoft SQL Server, preferably SQL Server 2016/2017 or above.
• Hands-on experience in developing, deploying and maintaining SSIS packages.
• Strong understanding of ETL processes, data integration and data migration.
• Advanced knowledge of T-SQL, stored procedures, functions, views and triggers.
• Experience in database performance tuning, query optimisation, indexing and execution plans.
• Experience in SQL Server monitoring, troubleshooting and resolving database performance issues.
• Good understanding of SQL Server Agent jobs, database backups, recovery and maintenance.
• Experience resolving database locking conflicts, blocking and resource utilisation issues.
• Knowledge of SQL Server system tables and Dynamic Management Views (DMVs).
• Experience with PowerShell scripting and automation is preferred.
• Exposure to AWS RDS, Azure SQL or other cloud database environments is an advantage.
• Knowledge of PostgreSQL or NoSQL databases is a plus.
• Experience leading or mentoring junior developers is preferred.
• Bachelor's or Master's degree in Computer Science, Software Engineering, IT or a related field.
• Excellent analytical, logical, troubleshooting and communication skills.

Good to Have

• Experience with SSRS or SSAS.
• Knowledge of AWS RDS or Azure SQL.
• Experience with PowerShell scripting.
• Exposure to PostgreSQL or NoSQL databases.
• Relevant Microsoft SQL Server certifications.

Why Navtech?

• Performance reviews and appraisals twice a year.
• Competitive compensation package with additional bonuses and benefits.
• Opportunities to work with renowned clients across the US, UK and Europe.
• Medical insurance coverage for self and immediate family.
• Opportunity to work with a culturally diverse and collaborative team.
• Opportunities for continuous learning and technical growth.

We are seeking a highly skilled Python Programmer and Insurance Analytics Lead to design, develop, automate, and operationalize predictive model development pipelines for Property & Casualty (P&C) Insurance. The role involves building reusable Python frameworks, feature engineering pipelines, MLOps solutions, automated model deployment and monitoring, and scalable analytics platforms supporting pricing, underwriting, claims, fraud detection, retention, and risk analytics.

Key Responsibilities

• Design, develop, and maintain scalable Python frameworks and automated predictive modeling pipelines for P&C insurance.
• Build robust feature engineering pipelines and implement MLOps practices for model deployment, monitoring, and lifecycle management.
• Drive insurance rating modernization by transforming legacy rating engines and integrating predictive models into pricing platforms.
• Collaborate with Data Scientists, Actuaries, Cloud Engineers, and business stakeholders to deliver scalable insurance analytics solutions.
• Lead ETL development and optimization initiatives across multiple teams, improving data integration, quality, and processing efficiency.
• Drive strategic initiatives to enhance ETL practices, establish best practices, and develop long-term technology roadmaps.
• Lead and mentor a team of 5+ professionals, ensuring effective delivery and technical excellence.
• Represent the ETL team in executive-level meetings and collaborate with cross-functional stakeholders on strategic initiatives.
• Support analytics solutions across pricing, underwriting, claims, fraud detection, customer retention, and risk management.

Required Skills & Qualifications

• 9–12 years of experience in Python development, ETL, data engineering, or data management.
• Strong hands-on expertise in Python programming, including Pandas, NumPy, object-oriented programming (OOP), classes, and coding best practices.
• Experience developing automated data pipelines, ETL frameworks, and scalable data processing solutions.
• Working knowledge of AWS cloud services, particularly Amazon S3 and data integration.
• Strong SQL skills and experience with database integrations and data processing.
• Experience in Property & Casualty (P&C) insurance analytics, insurance pricing, rating engines, underwriting, claims, or risk analytics.
• Understanding of predictive modeling pipelines, feature engineering, and MLOps practices.
• Experience leading teams of 5+ professionals and driving cross-functional technical initiatives.
• Strong stakeholder management, communication, and strategic planning skills.

Preferred Qualifications

• Experience in insurance rating modernization and integrating predictive models into pricing platforms.
• Exposure to model deployment automation, monitoring, and scalable analytics platforms.
• Experience collaborating with actuarial, data science, and cloud engineering teams.
• A bachelor's or master’s degree in computer science, Data Science, Information Technology, or a related field.

Location & Travel

• Locations: New Delhi (Gurugram), Bangalore, and Pune.
• Candidates may be required to travel to Gurugram on a quarterly basis, depending on client requirements.

Shift: Second Shift (2:00 PM – 10:00 PM)

Joining: Immediate

About the Role

We are looking for an experienced Data Integration Developer with strong hands-on expertise in SQL, Python, ETL development, Snowflake, and AWS data services.

The ideal candidate should have extensive experience in designing, developing, and maintaining data integration solutions, building data warehouse architectures, and implementing scalable ETL pipelines. Strong SQL development and performance optimization skills are essential for this role.

This is a remote position; however, candidates must be available to visit our Mumbai or Bangalore location for three days during the onboarding process.

Key Responsibilities

SQL Development & Performance Optimization

• Develop complex SQL queries, stored procedures, and data transformation logic.
• Optimize SQL queries for performance, scalability, and efficiency.
• Perform advanced data analysis, validation, and reconciliation.
• Troubleshoot complex SQL and data-related issues.

ETL Development & Data Integration

• Design, develop, and maintain ETL pipelines and data integration workflows.
• Work with SQL and Python for data extraction, transformation, and loading.
• Ensure data accuracy, consistency, and integrity across systems.
• Identify and resolve data pipeline failures and integration issues.

Data Warehousing & Data Modeling

• Design and maintain data warehouse architectures.
• Develop data warehouse schemas based on business requirements.
• Work on dimensional data modeling, including Facts and Dimensions.
• Implement Slowly Changing Dimensions (SCD).
• Support data analysis and business information reconciliation.

Snowflake & AWS

• Develop and manage data solutions using Snowflake.
• Work with AWS services, particularly Amazon S3.
• Manage AWS connections and data access configurations.
• Build and maintain cloud-based data integration solutions.

Python & Apache Airflow

• Develop data processing and automation solutions using Python.
• Build, schedule, and monitor workflows using Apache Airflow.
• Troubleshoot workflow failures and improve pipeline reliability.

Agile & Development Lifecycle

• Participate in Agile development processes, sprint planning, and team discussions.
• Contribute throughout all stages of the software development lifecycle.
• Collaborate with cross-functional teams to understand business and technical requirements.
• Perform root cause analysis and implement appropriate corrective actions.

Required Skills

• Advanced SQL – Strong hands-on expertise is mandatory.
• Python Development
• ETL Development and Data Integration
• Snowflake
• Apache Airflow
• AWS Services, particularly Amazon S3
• AWS Connection Management
• Data Warehouse Architecture
• Data Modeling (Facts & Dimensions)
• Slowly Changing Dimensions (SCD)
• SQL Query Optimization and Performance Tuning
• Data Analysis and Reconciliation
• Troubleshooting and Root Cause Analysis
• Agile Development Methodologies
• Strong understanding of the Software Development Life Cycle (SDLC)

Experience & Qualifications

• 8–10 years of overall experience in Data Engineering / Data Integration / ETL Development.
• Strong professional experience in advanced SQL and Python.
• Hands-on experience with Snowflake, Apache Airflow, and AWS.
• Proven experience designing and developing data warehouse solutions.
• Experience working across the complete product development lifecycle.
• Ability to independently troubleshoot complex technical challenges.

Additional Requirements

• Immediate joiners preferred.
• Must be comfortable working in the second shift (2:00 PM – 10:00 PM).
• Position is remote, with a mandatory three-day onboarding visit to Mumbai or Bangalore.
• Strong communication, analytical, and problem-solving skills.
• Ability to work independently and collaboratively in an Agile environment.

🚀 Job Title : Data Engineer – AWS, PySpark & Airflow

Experience : 5+ Years

Industry : Health Insurance / BFSI

Location : Sector 43, Gurugram, Haryana

Work Mode : Work From Office (5 Days a Week)

Employment Type : Full-Time

Role Overview :

We are looking for an experienced Data Engineer with strong hands-on expertise in AWS Data Engineering, Data Lake Architecture, Python, SQL, PySpark, and Apache Airflow.

The candidate will be responsible for designing, developing, and maintaining scalable, reliable, and high-performance data pipelines using AWS cloud technologies. The role involves building end-to-end ETL / ELT workflows, implementing data transformation and processing solutions, optimizing pipeline performance, and ensuring the reliability of production data systems.

Mandatory Skills :

AWS Data Engineering | Data Lake Architecture | Amazon S3 | AWS Glue | Amazon EMR | Athena | Redshift | Python | SQL | PySpark | Apache Spark | Apache Airflow | ETL/ELT | PostgreSQL | Data Modelling | Pipeline Optimization | Production Support | Root Cause Analysis

Key Responsibilities :

• Design, develop, and maintain end-to-end data pipelines for data ingestion, staging, transformation, curation, and consumption.
• Build and manage AWS-based data lake solutions using Amazon S3, AWS Glue, Amazon EMR, Athena, and Redshift.
• Develop, maintain, and optimize ETL / ELT pipelines using Python, SQL, Apache Spark, and PySpark.
• Implement and manage workflow orchestration using Apache Airflow, including scheduling, dependency management, retries, monitoring, and failure handling.
• Integrate data from multiple structured and semi-structured sources.
• Design and maintain data models, dimensional models, and analytical data marts.
• Work with relational databases, including PostgreSQL and Amazon RDS.
• Optimize Spark jobs, SQL queries, data transformations, and cloud resource utilization.
• Implement data quality checks, validation rules, and error-handling mechanisms.
• Monitor production data pipelines, troubleshoot failures, and perform root cause analysis (RCA).
• Ensure pipeline reliability, scalability, performance, and compliance with service-level agreements (SLAs).
• Follow data security, access management, governance, and sensitive-data protection best practices.
• Collaborate with business, analytics, and application teams to deliver effective data solutions.
• Maintain technical documentation and follow established data engineering standards.

Required Technical Skills :

• AWS Data Engineering : Strong hands-on experience with AWS-based data engineering and data lake architecture.
• AWS Services : Amazon S3, AWS Glue, Glue Data Catalog, Amazon EMR, Athena, Redshift, AWS Lambda, and IAM.
• Programming : Strong proficiency in Python and SQL.
• Big Data Processing : Hands-on experience with Apache Spark and PySpark.
• Workflow Orchestration : Experience developing and managing workflows using Apache Airflow.
• ETL/ELT Development : Experience building scalable data ingestion, transformation, and processing pipelines.
• Database Technologies : Good understanding of relational databases and PostgreSQL concepts.
• Data Modelling : Experience with data modelling, data transformation, and analytical data structures.
• Production Support : Ability to monitor pipelines, troubleshoot failures, perform RCA, and resolve production issues.
• Performance Optimization : Experience optimizing SQL queries, Spark jobs, and data processing workflows.

Good-to-Have Skills :

• Experience with AWS Database Migration Service (DMS) and Amazon RDS.
• Familiarity with dbt for data transformation and modelling.
• Knowledge of Delta Lake, Apache Iceberg, or Apache Hudi.
• Exposure to Apache Kafka and real-time data streaming.
• Familiarity with GraphQL, Hasura, or similar data-serving technologies.
• Understanding of data governance and data quality frameworks.
• Experience in the BFSI, healthcare, or health insurance domain.

Candidate Requirements :

• 5+ years of relevant experience in Data Engineering.
• Proven hands-on experience building and maintaining production-grade data pipelines on AWS.
• Strong understanding of data lake architecture and large-scale data processing.
• Practical experience with Apache Airflow and Spark / PySpark-based transformations.
• Strong programming, analytical, debugging, and problem-solving skills.
• Experience handling production incidents, pipeline failures, and performance bottlenecks.
• Ability to take ownership of deliverables and work effectively with cross-functional teams.
• Good communication, collaboration, and technical documentation skills.
• Willingness to work from the Gurugram office five days a week.

Ideal Candidate :

The ideal candidate is a hands-on Data Engineer who can independently build, optimize, and maintain AWS-based data pipelines, work with large datasets using PySpark, orchestrate workflows through Airflow, and resolve production issues in a business-critical environment.

I’m a Google Cloud Data Engineer with 5+ years of experience in data engineering, ETL, and cloud data solutions. I specialize in BigQuery, Dataform, Cloud Composer (Airflow), SQL, and CI/CD automation.

I have hands-on experience building reusable Dataform frameworks, developing and orchestrating data pipelines, supporting cloud platform upgrades, implementing secure IAM access, and optimizing data transformation workflows. I’ve also worked on AI-assisted SQLX code generation to improve developer productivity and standardize development.

I’m passionate about building scalable, reliable, and efficient data solutions. I’m looking for opportunities where I can contribute my GCP expertise, solve real-world data challenges, collaborate with talented teams, and continue growing as a Data Engineer.

Core skills: Google Cloud Platform (GCP) | BigQuery | Dataform | Cloud Composer | Apache Airflow | SQL | Python | ETL | CI/CD | GitHub Actions | Dataplex

Hiring: 10 openings for Associate Data Forward Deployed Engineer (0–2 yrs) for a fast-growing AI platforms & services company, Hyderabad (confidential client) via Ziro.digital.

Location: Hyderabad (onsite, full-time). Open to candidates from anywhere in India willing to relocate. Experience: 0–2 years. 2024/2025/2026 graduates welcome (B.Tech/B.E/MCA/M.Sc CS/IT or related).

What you'll do: build data pipelines end to end (ingest, transform, model, deliver), work with clients on their data, and use AI for query generation, data quality and debugging.

Must have: strong SQL (write, read, tune); Python for data; SQL Server and/or Postgres; at least one end-to-end data pipeline; data-analysis fundamentals; a GitHub with data projects.

Good to have: S3/Azure Blob, Airflow/mage.ai, DBT/Fabric, Snowflake/Databricks/BigQuery, GitLab CI/CD, Grafana, LLM/RAG awareness.

Compensation & terms: 6-month paid internship at ₹20,000/month, then ₹7 LPA CTC on conversion. Relocation to Hyderabad required.

Selection: Ziro assessment (Communication, Technical, AI interview, Coding), then the company's rounds (aptitude, AI screening, project build, in-office hackathon, technical panel, leadership).

Apply by Wed 21 Oct 2026.

Screening questions (please answer in your application):

1) Willing to relocate to Hyderabad and work onsite?

2) Do you accept a 6-month internship at ₹20k/month, then ₹7 LPA?

3) GitHub link + one project you built.

4) Graduation year, current city, notice period.

About Solutionara

Solutionara is an AI-led IT Consulting and Technology Services organization delivering enterprise transformation through Cloud, Integration, Data Engineering, Digital Experience, AI, and Enterprise Applications.

Headquartered in the United States with a Global Delivery Center (GDC) in Noida, India, Solutionara partners with enterprise organizations across retail, manufacturing, distribution, consumer goods, and other industries to design, implement, and support scalable technology solutions.

As a rapidly growing consulting organization, we foster a culture of ownership, innovation, collaboration, and continuous improvement, empowering our teams to contribute beyond their immediate responsibilities while delivering exceptional value to our global clients.

Every team member is expected to think like a consultant and business problem solver—understanding client objectives, challenging assumptions when appropriate, leveraging AI responsibly, and continuously identifying opportunities to improve quality, productivity, and customer outcomes.

AI is embedded into how we work, but it is an enabler—not a replacement for expertise, critical thinking, or accountability.

Role Overview

Solutionara is seeking a Senior Data Engineer with strong data engineering and database expertise to design, build, optimize, and support enterprise-scale data solutions.

This is a hands-on engineering position requiring strong SQL, Python, ETL/ELT, database, cloud, and troubleshooting capabilities. The engineer will work closely with technical leads and client stakeholders while taking ownership of major engineering components.

Location: Noida, India – Onsite | Employment Type: Full-Time | Experience: 6–9 Years

Key Responsibilities

Data Engineering

• Design, develop, test, deploy, and support scalable ETL/ELT pipelines.
• Build ingestion and transformation solutions across databases, APIs, files, and enterprise applications.
• Develop high-quality SQL and Python-based data processing solutions.
• Implement batch and incremental data-processing patterns.
• Develop reusable engineering components and frameworks.
• Support complex data migration and transformation activities.
• Perform source-to-target mapping and technical data analysis.
• Implement pipeline logging, monitoring, exception handling, and recovery mechanisms.

Database & Platform Engineering

• Design and optimize relational database schemas and data structures.
• Analyze and improve SQL/query performance.
• Support indexing, partitioning, storage optimization, and database performance.
• Implement appropriate database security and access controls.
• Support database monitoring, availability, backup/recovery, and operational requirements in collaboration with infrastructure/platform teams.
• Troubleshoot database and pipeline performance issues.
• Contribute to cloud data-platform configuration and optimization.

Data Quality

• Implement automated validation and data-quality controls.
• Develop reconciliation processes between source and target systems.
• Identify duplicate, incomplete, inconsistent, and invalid data.
• Implement cleansing, standardization, validation, and exception-management logic.
• Work with technical and business stakeholders to understand and implement business data rules.

Technical Delivery

• Convert technical designs into reliable production solutions.
• Participate in technical design and code reviews.
• Mentor junior/mid-level engineers.
• Support estimation and technical planning.
• Troubleshoot complex production and integration issues.
• Collaborate with technical leads, application teams, DevOps, QA, BI, and client teams.
• Document technical designs, mappings, configurations, operational procedures, and engineering decisions.

Consulting & AI-Led Working

• Understand the business purpose behind assigned engineering requirements.
• Raise risks, assumptions, dependencies, and potential improvements proactively.
• Communicate effectively with client and internal stakeholders.
• Use AI-assisted tools to improve coding, testing, analysis, documentation, and troubleshooting productivity.
• Independently verify AI-generated code and recommendations before implementation.
• Identify opportunities for engineering automation and process improvement.

Required Qualifications

• 6–9 years of experience in data engineering, database engineering, data integration, or related areas.
• Advanced SQL skills.
• Strong hands-on Python experience.
• Strong ETL/ELT development experience.
• Strong relational database knowledge, such as SQL Server, PostgreSQL, Oracle, or equivalent.
• Experience with data modeling and database design.
• Experience with AWS, Azure, or another major cloud platform.
• Experience with modern data technologies such as Databricks, Snowflake, Redshift, Synapse, Glue, ADF, Airflow, dbt, or equivalent.
• Experience with performance tuning and optimization.
• Experience with data validation and reconciliation.
• Strong troubleshooting and analytical skills.
• Ability to take ownership of engineering deliverables with limited supervision.

Preferred Qualifications

• Experience working with enterprise ERP, CRM, PIM, PLM, MES, or related systems.
• Experience with manufacturing, retail, distribution, or consumer-goods data.
• Experience with CI/CD and infrastructure/platform engineering practices.
• Experience with API and event-driven integrations.
• Previous consulting or technology-services experience.

What Success Looks Like

• Pipelines and databases perform reliably at enterprise scale.
• Engineering solutions are maintainable, reusable, and well documented.
• Data issues are detected and addressed systematically.
• Junior engineers receive effective technical guidance.
• Problems are owned through resolution rather than simply escalated.
• Engineering decisions reflect both technical requirements and client business objectives.

Job Description: Informatica IDMC & Snowflake Developer

Experience: 6+ Years

Required Skills: Informatica IDMC, ETL, Data Warehousing, Snowflake, SQL

Contract Duration: 2 months

Required Technical Skills

• Informatica: Strong hands-on experience with Informatica IDMC, Cloud Data Integration (CDI), Cloud Application Integration (CAI), Mass Ingestion (MI).
• ETL / ELT: Strong understanding of ETL architecture, data integration patterns, incremental loading, and error-handling strategies.
• Data Warehousing: Good understanding of dimensional modelling, data marts, fact and dimension tables, and data warehouse design principles.
• Snowflake: Hands-on experience with Snowflake architecture, SQL, data loading, transformations, performance tuning, and warehouse optimization.
• SQL: Strong proficiency in complex SQL queries, analytical functions, views, stored procedures, and query optimization.
• Database Development: Experience creating and maintaining database objects, validating data, reconciling datasets, and troubleshooting performance issues.

Candidate Profile

• 6+ years of hands-on experience in ETL/ELT development and data integration along with IDMC, Snowflake, Data Warehousing and SQL.
• Strong analytical, problem-solving, and communication skills.
• Ability to work independently, manage priorities, and collaborate effectively with cross-functional teams.
• Proven ability to deliver high-quality, scalable, and maintainable data solutions in an Agile Environment.

We are looking for a dynamic Data Engineer to join our team of technology enthusiasts. You will leverage data to drive strategic decision-making and pioneering solutions, working with complex datasets, collaborating closely with stakeholders, and transforming data into actionable insights to drive innovation.

Qualifications and Skills:

• Minimum 5 years of experience as a Data Engineer
• Hands-on experience with Azure cloud-based data solutions
• Expertise in database design and management, including SQL databases such as SQL Server
• Proficient in ETL (Extract, Transform, Load) design for data integration and processing
• Strong knowledge of data modeling principles and techniques
• Ability to analyze and translate data into actionable insights, reports, and visualizations
• Proficiency in Power BI for reporting and data visualization

Desirable Skills:

• Experience with Power BI Report Builder / Reporting Services
• Knowledge of statistical analysis or Data Science
• Experience within the UK Insurance industry is a plus
• Python or R coding skills

Responsibilities:

• Implement efficient data exchange between internal and external systems to increase efficiency and reduce re-keying and translation errors
• Support the Broking business by developing high-quality information resources, ensuring data availability and accessibility for decision-making
• Engineer data inputs and outputs from core applications and semi-structured remote service data through data syncs between data lake, ODS (SQL database), and leveraging Fabric and ADF
• Perform data engineering tasks including ingestion, cleansing, and collation from a wide range of internal and external sources
• Implement different methods of streaming data and create reconciliations for datasets
• Build analytical models to support reporting and analytics
• Collaborate with an agile delivery team to work on the backlog of specified work

8-12 years experience on Data Engineering role working with Databricks & Azure Cloud technologies.

• Bachelor’s degree in computer science, Information Technology, or related field.

• Strong proficiency in PySpark, Python, SQL.

• Strong experience in data modeling, ETL/ELT pipeline development, and automation • Hands-on experience with performance tuning of data pipelines and workflow

• Proficient in working on Azure cloud components Azure Data Factory, Azure DataBricks, Azure Data Lake etc.

• Experience with data modeling, ETL processes, Delta Lake and data warehousing.

• Experience on Delta Live Tables, Autoloader & Unity Catalog.

Preferred - Knowledge of the insurance industry and its data requirements.

• Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.

• Excellent communication and problem-solving skills to work effectively with diverse teams

• Excellent problem-solving skills and ability to work under tight deadlines. 

Job Title : Data / AI / GenAI – Engineering & Analytics

Experience : 4+ Years

Location : Noida / Gurugram / Ahmedabad

Employment Type : Full-time | On-site at client location

Working Days : 5 days per week

Working Hours : Standard business hours

Position Overview : We are seeking professionals to deliver modern data engineering, business intelligence, cloud analytics and AI-powered applications, according to their specialization.

Mandatory Skills :

Python, SQL, ETL/ELT, Data Engineering, Power BI, Azure Data Factory, Databricks, PySpark, Data Modeling, GenAI/LLMs, RAG, Vector Databases, AI Agents (as per role specialization).

Key Roles / Specializations :

1. Power BI Developers : Dashboards, DAX, Power Query, reporting and data modeling
2. Azure Data Engineers : Azure Data Factory, Data Lake, Azure SQL and cloud pipelines
3. Databricks Engineers : Apache Spark, PySpark, Delta Lake and scalable processing
4. Data Engineers : ETL/ELT, SQL, Python, data warehousing and quality
5. GenAI / RAG Engineers : LLMs, prompts, embeddings, vector databases and retrieval pipelines
6. Agentic AI Engineers : AI agents, tool calling, orchestration and multi-agent workflows

Key Responsibilities :

• Design, develop and maintain data pipelines, analytics solutions or AI applications.
• Build and optimize data integration, transformation and processing workflows.
• Develop dashboards and analytical models for business decision-making.
• Use cloud data platforms and modern engineering frameworks.
• Develop and evaluate LLM applications, RAG solutions or AI agents as applicable.
• Ensure data quality, security, performance and reliability.
• Collaborate with engineering teams and stakeholders on testing, deployment and documentation.

Required Skills & Qualifications :

• Bachelor’s degree in Computer Science, IT, Data Science, Engineering or related discipline.
• 4–5 years of relevant experience in data, analytics, AI or software engineering.
• Hands-on expertise in at least one listed specialization; not all technologies are required.
• Relevant Python, SQL, DAX or other technical skills depending on the position.
• Understanding of cloud platforms, APIs, integration and software practices.

Preferred Qualifications :

• Azure, Databricks or Microsoft Fabric experience.
• For GenAI roles : Azure OpenAI, OpenAI APIs, LangChain, LlamaIndex or LangGraph.
• For relevant roles : vector databases, CI/CD, Docker, MLOps or LLMOps.

Note : Candidates are expected to have expertise in their relevant specialization, not in every listed technology or role.

Job Summary – Axiom Technical Analyst

🔹 Experience: 5+ Years

🔹 Skill Set: Axiom + SQL + ETL

🔹 Locations: Bangalore / Hyderabad

🔹 Notice Period: Immediate to 15 Days Joiners Preferred

Required Skills:

🔹 Strong hands-on experience in Axiom

🔹 Good knowledge of SQL and database concepts

🔹 Hands-on experience with ETL processes

🔹 Experience in data extraction, transformation, and loading

🔹 Good understanding of data integration and data processing

🔹 Experience in developing, maintaining, and troubleshooting data workflows

🔹 Strong analytical and problem-solving skills

🔹 Good communication and collaboration skills

🔹 Ability to troubleshoot technical issues and support application/data processes

🔹 Understanding of SDLC and Agile methodologies

Hiring: ETL Developer – Hadoop & PySpark

Location: Bangalore

Experience: 6–10 Years

Budget: 25–30 LPA

Work Mode: Weekdays

Job Description

We are looking for an experienced ETL Developer with strong expertise in Hadoop, PySpark, Python, and SQL to design, develop, and optimize data processing and transformation solutions. The ideal candidate should have hands-on experience working with big data technologies, building scalable ETL pipelines, and managing data for data warehousing and analytics.

Key Responsibilities

• Design, develop, and maintain scalable ETL pipelines for large-scale data processing.
• Develop data transformation workflows using PySpark and Python.
• Work with Hadoop and other big data technologies to process and manage large datasets.
• Write complex SQL queries for data extraction, transformation, validation, and analysis.
• Implement data cleansing, data quality checks, and data validation processes.
• Develop and optimize data integration workflows for data warehousing solutions.
• Troubleshoot ETL failures, performance issues, and data discrepancies.
• Optimize PySpark jobs and SQL queries for performance and scalability.
• Collaborate with data engineers, architects, analysts, and other stakeholders to deliver reliable data solutions.
• Ensure data accuracy, consistency, and adherence to established data governance standards.

Mandatory Skills

• Strong hands-on experience in ETL development.
• Good experience with Hadoop and big data technologies.
• Proficiency in PySpark and Python programming.
• Strong SQL skills, including complex queries and data manipulation.
• Experience in data processing, data transformation, and data integration.
• Understanding of data warehousing concepts and architecture.
• Experience in developing, debugging, and optimizing ETL pipelines.

Required Experience

• 6–10 years of relevant experience in ETL development and data engineering.
• Practical experience handling large datasets using Hadoop and PySpark.
• Strong analytical, problem-solving, and troubleshooting skills.
• Ability to work independently and collaborate effectively within a team.

Interested candidates can share their updated resumes along with current CTC, expected CTC, notice period, and total relevant experience.

Dear Candidate,

We have an immediate opening with Mphasis for Senior Full Stack Engineer (Python AI).

📍 Location: Hyderabad / Bangalore

💼 Experience: 5+ Years

🏢 Employment: Permanent

⚡ Immediate Joiners Preferred

Mandatory Skills:

• Python
• Java + Spring Boot
• React.js
• MongoDB

GenAI

Position: Backup Data ETL Developer

Experience: 6–9 Years

Location:Mumbai

Employment Type: Full-time

Job Description:

We are looking for a Backup Data ETL Developer with 6–9 years of experience in designing, developing, and supporting data extraction, transformation, and loading (ETL) processes.

The candidate should have strong hands-on experience with Hadoop, Spark, Informatica, and Snowflake, along with expertise in large-scale data processing and data integration technologies.

Key Skills:

Hadoop

Spark

Informatica

Snowflake

ETL Development

Data Integration

Data Transformation

Large-Scale Data Processing

ETL Workflows and Data Pipelines

Troubleshooting and Problem-Solving

Strong analytical and collaboration skills

As a Solutions Engineer/Sales Engineer, you will act as a trusted technical advisor to prospective customers in the US market. You will own the technical evaluation journey end-to-end, from the first discovery call to closing the deal, by demonstrating how our platform solves real-world data integration challenges. This role involves working closely with global customers (primarily North America ), designing scalable architectures, running tailored product demos, and building proofs-of-concept that showcase value.

Responsibilities:

• Run technical discovery calls with Heads of Data, Data Engineering Managers, and CTOs to understand their stack, pain points, and constraints. These are global conversations, primarily with customers in North America and Europe, and you will own them end-to-end.
• Design and present reference architectures showing how Hevo fits into their existing data stack (e. g., Salesforce, Postgres, GA4 Snowflake, dbt).
• Run live product demos tailored to the customer's stack. These are not scripted walkthroughs. You will need to read the room, adapt on the fly, and handle unexpected technical questions with confidence.
• Own the technical evaluation from the first conversation to the closed deal by addressing deep technical questions and earning trust with engineering teams.
• Build proofs-of-concept by configuring real pipelines from source systems (Salesforce, Outreach, Postgres, MySQL) into destinations like Snowflake or BigQuery.
• Develop deep expertise in the data integration landscape, including native solutions from AWS, GCP, and Azure (AWS Glue, Azure Data Factory, Google Cloud Dataflow) as well as other data pipeline and integration platforms, and articulate Hevo's technical advantages in a way that lands with both technical and non-technical buyers.
• Partner with Account Executives to drive technical wins and with Customer Success to ensure smooth handoffs after a deal closes.
• Share customer feedback and patterns with Product and Engineering to influence the roadmap.

Requirements:

• 2 to 6 years of hands-on experience as a Data engineer, Analytics engineer, ETL/ELT developer, or Data warehouse engineer.
• Experience building and operating real data pipelines in production, including handling source schema changes, failures, and reliability issues.
• Strong fundamentals in SQL and at least one cloud data warehouse (Snowflake, BigQuery, or Redshift).
• Experience with at least one orchestration or transformation tool (Airflow, dbt, or similar).
• Exceptional communication and presentation skills.
• This is non-negotiable. You will be the face of Hevo's technical capability to a global customer base. That means:
• Your spoken English is clear, confident, and easy to follow for an international audience.
• You are comfortable presenting to groups of 5 to 15 people, including senior technical leaders, over video calls.
• You can structure a technical narrative, not just answer questions but guide a conversation toward the right conclusion.
• You can hold your own when a customer pushes back or challenges your recommendation.
• You have presented or demoed something technical to an audience before, in any context: internal reviews, client calls, meetups, or conferences.
• Genuine interest in customer-facing work, even if you have not done it formally before.

Good to have:

• Experience with CDC tools or data integration platforms, whether cloud-native (AWS Glue, Azure Data Factory, or Google Cloud Dataflow) or third-party pipeline and integration tools.
• Exposure to multiple source system types such as CRMs, ad platforms, SaaS APIs, and OLTP databases.
• Public technical writing, conference talks, or open-source contributions.
• Prior experience in customer conversations, even informally. For example, presenting to internal stakeholders or analytics teams or working with international teams across time zones.

Job Title: Senior Data Engineer

Position Summary

We are seeking a highly skilled Data Engineer with strong expertise in reporting infrastructure, data modeling, and analytics platforms. This role will focus on building scalable reporting solutions, optimizing data pipelines, and enabling data-driven decision-making across the organization. The ideal candidate will have deep experience with databases, data warehousing, and complex query development. While Ruby on Rails experience is beneficial, the primary emphasis is on data engineering and reporting systems.

Key Responsibilities

• Design, develop, and maintain reporting infrastructure and data platforms.
• Build and optimize complex SQL queries, data models, and reporting datasets.
• Develop scalable ETL/ELT pipelines and data integration processes.
• Design and maintain database schemas to support analytics and reporting requirements.
• Collaborate with business stakeholders to understand reporting needs and translate them into technical solutions.
• Implement data quality, governance, and performance best practices.
• Support and enhance enterprise reporting solutions and dashboards.
• Work with large-scale datasets in cloud data warehouse environments.
• Troubleshoot reporting issues and optimize database performance.
• Partner with engineering and product teams to establish sustainable data architecture.

Must Have: Technical Skills

• PostgreSQL
• Snowflake
• Advanced SQL
• Data Modeling
• Data Warehousing
• Reporting & Analytics Infrastructure
• ETL/ELT Development
• Query Performance Tuning

Success Profile:

The ideal candidate is a data-focused engineer who enjoys working with complex datasets, designing scalable reporting solutions, and building foundational infrastructure that empowers analytics and business intelligence teams. They should be comfortable navigating databases, optimizing queries, and translating business reporting needs into robust technical implementations.

Experience Range: 5-10 years

Role Type: Individual Contributor / Senior Engineer

Primary Focus: Reporting Infrastructure, Data Engineering, Data Warehousing, and Analytics Enablement.

Responsibilities and JD

Job Description: We are looking for a Senior Developer with strong expertise in PySpark, Databricks, and Snowflake to build scalable data engineering solutions and enterprise data platforms.

Key Responsibilities:

1. Design, develop, and maintain ETL/ELT pipelines using PySpark, Databricks, and Snowflake.
2. Develop batch and real-time data processing solutions for structured and semi-structured data.
3. Build and optimize Databricks notebooks, workflows, and Delta Lake solutions.
4. Design and implement Snowflake databases, schemas, views, stored procedures, tasks, and streams.
5. Develop scalable data models, data marts, and data warehouse solutions.
6. Optimize PySpark jobs, Databricks workloads, and Snowflake queries for performance and cost efficiency.
7. Implement data quality, validation, governance, and security controls.
8. Collaborate with business stakeholders, architects, and cross-functional teams to deliver data solutions.
9. Manage source control and CI/CD deployments using Git and Azure DevOps.
10. Troubleshoot production issues, perform root cause analysis, and ensure pipeline reliability.
11. Mentor junior team members and participate in code reviews and technical design discussions.

Required Skills: PySpark, Databricks, Snowflake, Python, SQL.

Experience: 5+ years of Data Engineering experience with strong hands-on expertise in PySpark, Databricks, and Snowflake.

🚨 Hiring: MicroStrategy Developer

We are looking for an experienced MicroStrategy Developer for a 6-month contract opportunity.

📌 Role: MicroStrategy Developer

💼 Experience: 5+ Years

📍 Location: Bangalore / Hyderabad / Mumbai / Delhi

🏢 Work Mode: Onsite

📅 Contract: 6 Months

Key Requirements:

• 5+ years of hands-on MicroStrategy experience

• Strong experience in MicroStrategy development

• Strong SQL skills

• Experience in BI reporting and dashboard development

• Experience with data warehousing and analytics

• Ability to understand business requirements and develop reporting solutions

• Experience in troubleshooting and supporting MicroStrategy applications

Interested candidates can apply with their updated resume.

We are seeking an experienced Informatica MDM Developer to design, develop, and implement Master Data Management (MDM) solutions using Informatica IDMC (SaaS). The role focuses on establishing a Single Source of Truth by integrating multiple data sources, enforcing data quality standards, and managing master data domains such as Customer, Product, Supplier, and Employee.

The engineer will collaborate closely with data stewards, data architects, and business stakeholders to deliver high-quality, governed enterprise data. The role requires willingness to support production MDM SaaS platforms and the data warehouse feeding MDM. When capacity permits, the candidate will also support GCP Data Engineering activities

Key Roles & Responsibilities

• Design and build Master Data Management (MDM) solutions using Informatica IDMC (SaaS).
• Create and manage data models for domains like Customer, Product, Supplier, and Employee.
• Configure Business 360 including UI, entities, and business rules.
• Develop and maintain match, merge, and survivorship rules to create accurate golden records.
• Build data integrations using Cloud Data Integration (CDI) and Cloud Application Integration (CAI).
• Develop and support REST APIs for real-time data exchange.
• Implement data quality, cleansing, and standardization rules using Cloud Data Quality (CDQ).
• Support data stewards in resolving data issues, merges, and exceptions.
• Monitor, troubleshoot, and optimize MDM SaaS platform performance.
• Support production MDM systems and the data warehouse feeding MDM.
• Collaborate with business and technical teams to deliver high-quality governed data.
• Willing to support GCP Data Engineering tasks when required (training provided).

Required Skills & Qualifications:

• Hands-on experience with Informatica IDMC (SaaS) including Business 360, Customer 360, or Product 360.
• Strong expertise in CDI, CAI, and CDQ.
• Solid understanding of relational and dimensional data modeling.
• Advanced SQL skills.
• Experience with REST/SOAP APIs, JSON, and XML.
• Familiarity with cloud data platforms such as Snowflake, Databricks, AWS Redshift, or Azure Synapse.

Experience & Education

• Education: Bachelor’s degree in Computer Science, Information Technology, or a related field.

Data Engineer Hiring Post

🚨 Hiring: Data Engineer | PySpark + Python + SQL

We are looking for experienced Data Engineers to join our team!

🔹 Experience: 5 to 9 Years

🔹 Locations: Bangalore / Hyderabad

🔹 Interview Process:

• 1st Round – Virtual

• 2nd Round – Face-to-Face (Karat Test)

🔑 Key Skills:

✅ PySpark

✅ SQL

✅ Python

✅ ETL

📩 Interested candidates can share their updated resume.

#Hiring #DataEngineer #PySpark #Python #SQL #ETL #BangaloreJobs #HyderabadJobs #TechHiring #ImmediateHiring

Job Description:

Skill Set

Informatica, IDMC, Informatica MDM

Experience

4-6 Years

Location

Faridabad, Haryana, India

Work Mode- Hybrid

Job Description

Roles & Responsibilities

• Lead project team members through all activities required to successfully deliver Informatica.
• Define technical specifications for workflows and business rules.
• Prepare detailed design documents for data migration.
• Configure, build and unit test Informatica components, including data structures, roles, workflows, and portals.
• Train and mentor colleagues in Informatica implementation activities and best practices.
• Support integration, system, and user acceptance testing.
• Accountable for the Master Data Management, Technical Architecture, providing data modeling solutions and recommendations.
• Profile & Analyze Source Data usage using ETL tool.
• Automation, job scheduling, dependencies, monitoring.
• Configure/Script Business rules and transformation rules in ETL
• Data pipeline using IICS
• Hands on experience in Informatica MFT, Data Exchange (DX),and Data Transform (DT) modules
• Integration experience using various adapters (MLLP, SOAP, REST, SFTP, ODBC) will be essential.
• Strong knowledge and experience with developing complex SQL Queries, stored procedures, and triggers for RDBMS systems.
• Experience in administering, supporting, and performance tuning using Informatica.

Hiring: Informatica IDMC / IICS Developer

Location: Faridabad, Haryana

Experience: 4–7 Years

Must-Have:

• Informatica IDMC / IICS

• Data Integration & Application Integration (CAI)

• ETL / Data Pipelines / Data Migration

• REST, SOAP, SFTP & API Integration

• Strong SQL, Stored Procedures & RDBMS

• Job Scheduling, Monitoring & Performance Tuning

Good to Have: Informatica MFT, DX, DT, MDM & Data Quality

Data Engineer

Location: Bengaluru, India (Hybrid)

Employment Type: Full-time

Experience: 3-5 years

Role Overview  

What We’re Looking For:

• Bachelor’s degree in Computer Science/Engineering or equivalent experience required.
• Experience designing and shipping cloud services products.
• Experience driving and managing technical and architectural dependencies on AWS Cloud.
• A firm understanding of system architecture, cloud computing, PaaS/SaaS design principles, S3, DynamoDB, RDS mandatory.
• Experience in building or maintaining ETL processes and tools, i.e., AWS Glue or any open-source tool.
• Proven system-level design contribution to a current “Live” (in production / under daily high load) multi-region SaaS or PaaS offering.
• Proven experience with S3, DynamoDB, SQL, and AWS RDS services.
• Proficiency in programming languages such as Python.
• Strong analytical and problem-solving skills.
• 
  

Required Skills & Experience

• Experience with Python, SQL, and data visualization/exploration tools.
• Familiarity with the AWS ecosystem, specifically S3, DynamoDB, and RDS.
• Communication skills, especially for explaining technical concepts to nontechnical business leaders.
• Ability to work on a dynamic, research-oriented team that has concurrent projects.
• Experience in AWS cost optimization (Savings Plans, Reserved Instances, Spot Instances) and governance frameworks.
• Experience developing solutions using infrastructure orchestration tools (SSM, automation account, Ansible, etc.).
• Excellent leadership, stakeholder management, and communication skills.

What We Offer

• Work with some of the brightest minds in the emerging EV industry.
• Make a tangible impact in reducing carbon emissions and enabling sustainable energy.
• Freedom to suggest, implement, and innovate on systems, processes, and technologies.
• Daily ownership in a high-growth, challenging environment.
• Flexible work environment with hybrid schedules and virtualization options.
• Competitive pay and benefits including health coverage, innovative PTO program, and performance bonuses.

Job Summary

Role Overview

We are looking for an experienced Data Engineer with strong expertise in Python, ETL, Advanced SQL, CI/CD, DevOps, and Data Analytics. The ideal candidate should have hands-on experience designing and developing scalable data pipelines, transforming large datasets, and supporting data-driven applications.

Experience with Google Cloud Platform (GCP) will be an added advantage.

Key Responsibilities

• Design, develop, and maintain scalable ETL/ELT data pipelines using Python and SQL.
• Develop complex and optimized SQL queries, stored procedures, and data transformations.
• Build and maintain reliable data integration workflows across multiple data sources.
• Perform data cleansing, validation, transformation, and quality checks.
• Analyze data and provide insights to support business and technical requirements.
• Implement and maintain CI/CD pipelines for data engineering applications.
• Work with DevOps practices and tools to automate deployments, monitoring, and infrastructure processes.
• Troubleshoot data pipeline failures, performance issues, and production incidents.
• Optimize data processing workflows for performance, scalability, and reliability.
• Collaborate with Data Analysts, Data Scientists, Developers, and other stakeholders.
• Follow best practices for version control, testing, documentation, and deployment.
• Contribute to cloud-based data engineering initiatives, preferably on GCP.

Required Skills

• 5–7 years of hands-on experience in Data Engineering.
• Strong programming skills in Python.
• Strong expertise in Advanced SQL and database concepts.
• Hands-on experience with ETL/ELT processes and data pipelines.
• Good understanding of Data Warehousing and Data Modeling concepts.
• Experience with CI/CD practices and tools.
• Strong understanding of DevOps principles, automation, and deployment processes.
• Strong data analytics and problem-solving skills.
• Experience working with large datasets and performance optimization.
• Good understanding of Git/version control and software development best practices.

Good to Have

• Hands-on experience with Google Cloud Platform (GCP).
• Exposure to GCP data services such as BigQuery, Cloud Storage, Dataflow, Composer, or Pub/Sub.
• Experience with containerization/orchestration technologies such as Docker/Kubernetes.
• Experience with workflow orchestration tools such as Airflow.
• Knowledge of cloud-based data architecture and distributed data processing.

Preferred Candidate Profile

• Strong analytical and problem-solving abilities.
• Good communication and stakeholder management skills.
• Ability to work independently as well as in a collaborative team environment.
• Strong ownership of data pipelines and production systems.
• Candidates who can join at short notice are preferred.

Mandatory Skills

 Data Engineer, Python , ETL, GCP, Advanced SQL, Strong Data Analytics skills, CICD, Devops

Job Title : Senior Data Engineer – Databricks

Experience : 14 to 20 Years

Location : HSR Layout, Bangalore

Work Mode : Hybrid – 3 Days WFO

Shift : 11:30 AM – 07:30 PM IST

Positions : 2

Notice Period : Immediate Joiners Only

Interview : 1 Technical Round + 2 Client Rounds

Role Overview :

We are looking for a Senior Data Engineer to build and lead enterprise-scale data platforms for a Switzerland-based commodity client.

The role requires a strong hands-on Data Engineering professional with expertise in Databricks, PySpark, Python, SQL, and AWS, along with technical leadership and stakeholder management experience.

Must-Have Skills :

• 14 to 20 years of Data Engineering experience
• Databricks & Apache Spark / PySpark
• Python & SQL
• AWS Cloud
• Lakehouse Architecture
• ETL / ELT & Distributed Data Processing
• Batch & Streaming Pipelines
• Data Pipeline Optimization & Data Modeling
• CDC & Incremental Processing
• Git, CI/CD & Testing
• Data Quality, Monitoring & Observability
• Technical Leadership & Stakeholder Management

Key Responsibilities :

• Design and build scalable data pipelines using Databricks, PySpark, Python, SQL, and AWS.
• Own data products from design through production.
• Develop batch / streaming pipelines and reusable ETL / ELT frameworks.
• Optimize pipelines for performance, scalability, reliability, and cost.
• Design scalable data architectures and data models.
• Implement data quality, monitoring, lineage, and CI/CD practices.
• Lead technical discussions and mentor engineering teams.
• Collaborate with business stakeholders, architects, product owners, and engineering teams.
• Remain hands-on while providing technical leadership.

Ideal Candidate :

A 14 to 20 years experienced, hands-on Data Engineering leader with strong Databricks + PySpark + AWS expertise, excellent communication, stakeholder management, and experience delivering enterprise-scale data platforms.

🔴 Super Urgent : Only Bangalore-based immediate joiners.

Key Responsibilities

Build and maintain data transformation pipelines using java Spark

Develop and optimize large-scale/CPU intensive data processing using Apache Spark

Orchestrate workflows using Airflow

Implement data quality checks, testing, and monitoring for pipeline. Good to have exposer into managing metadata, cataloguing, and lineage

Support schema evolution, backfills, and incremental processing

Ensure pipelines meet SLAs for freshness, reliability, and performance

Expertise/working knowledge in Spark and HBase(semantic layer, virtual datasets, Reflections)

Required Skills & Qualifications

Strong hands-on experience with Apache Spark

Experience with HBase/SQL or similar lakehouse query engines

Airflow 

Understanding of data catalogs and lineage (e.g., OpenLineage, DataHub, Apache Polaris , openlineage)

Proficiency in Java

Experience with Git-based development and CI/CD

Key Responsibilities

1. Solutioning & Proposal Development

•   Partner with Senior SMEs and Practice Leads to design end-to-end Data & AI solutions for client pursuits — contributing to structure, content, and commercial framing.

•   Build client proposals, solution documents, and program structures that are well-organized, accurate, and ready to use without significant rework.

•   Translate client requirements into structured, outcome-oriented learning journeys — adoption, capability uplift, and measurable business outcomes, not just module lists.

•   Support customized offerings across Data Engineering, AI / ML, and GenAI and Agentic AI tracks; help assemble pursuits from existing accelerators rather than rebuilding from scratch.

2. Client Engagement Support

•   Participate in client discussions, discovery calls, and requirement-gathering sessions — capture context with the rigour that makes the next conversation sharper.

•   Convert business needs into solution frameworks and delivery models with guidance from Senior SMEs; document customer priorities so Practice and Sales can act on them.

•   Support pitch decks, case studies, and success stories — buyer-specific, visually clean, and aligned to how the customer thinks about their own problem.

•   Stay engaged through the proposal cycle and handoff to delivery; ensure no requirement gets lost between discovery and execution.

3. Content & Program Structuring

•   Assist in designing curriculum outlines, learning journeys, and hands-on lab structures that hold up against real-world enterprise contexts.

•   Work with internal and external SMEs to ensure content aligns with current industry trends, real use cases, and business outcomes — not generic technology overviews.

•   Maintain a library of reusable program structures, slide assets, and case study inserts; flag gaps in the existing content library proactively.

4. Research & Market Intelligence

•   Track trends across the AI / GenAI / LLM ecosystem and Data Engineering & Analytics — translate findings into usable inputs for outreach, pitching, and offering design.

•   Identify new solution opportunities and product ideas based on market signals, customer asks, and competitor moves.

•   Benchmark StackRoute's offerings against competitors; surface gaps and differentiation angles for Senior SMEs and Practice Leads to act on.

5. Internal Collaboration

•   Work fluidly with Delivery, Sales, and external SMEs to ensure solutions designed on paper actually work in delivery — surface feasibility risks early, not late.

•   Coordinate inputs across Practice teams during pursuit cycles; hand off to delivery with documentation that captures customer commitments and success metrics.

Must Have Technical & Functional Skills

•   Good understanding of terminologies in Data Engineering (ETL, pipelines, data lakes), Data Analytics & BI concepts, and Machine Learning fundamentals, AI tools ( not technical expertise but L1-L2 knowledge should be present from application standpoint).

•   Awareness of GenAI / LLM vocabulary — prompt engineering, RAG, APIs — with enough depth to hold a credible first conversation with a technical stakeholder.

•   Strong PowerPoint skills (client-ready decks), Excel for effort estimation and costing basics, and structured documentation — proposals, SoWs, one-pagers.

•   Strong problem-solving and structured thinking — breaks complex requirements into clear, communicable solutions.

•   Comfortable communicating with both technical and non-technical stakeholders; good storytelling and presentation instincts; understanding of L&D context is a plus.

Qualifications & Experience

Required:

•   5-15 years in the education products, or in solutioning, pre-sales, or consulting roles with exposure of 3-5 years in Data/AI/Analytics.

•   Bachelor's or Master's in Computer Science, Data Science, Engineering, or a related discipline.

Nice to Have competences:

•   Prior pre-sales, proposal writing, or design development experience.

•   Certifications in cloud, data, or AI platforms (AWS, Azure, GCP, or model-provider certifications).

Core Competencies

·      Structured Thinking: Organises ambiguous client and technical inputs into logical, buyer-relevant narratives. Builds proposals that flow from problem to solution.

·      Solution Articulation: Translates Data & AI capabilities into crisp, persona-specific stories. Adapts the pitch for a CTO, L&D Head, or BU Head without losing substance.

·      Research & Synthesis: Gathers and distils large volumes of information into sharp, usable outputs. Knows what to include and what to leave out.

·      Written Communication: Writes a tight brief, a clean slide, and a clear email. Adapts register from internal working notes to buyer-facing collateral.

·      Curiosity & Learning Agility: Picks up new tools, concepts, and sectors quickly. Tracks AI / GenAI shifts proactively rather than waiting to be told what to read.

·      Bias for Action: Ships a useful 10-slide deck on time rather than a polished 20-slide deck that's late. Comfortable with iteration over perfection.

Job Title: Senior Data Tester

Location : Hyderabad

Mode: Hybrid

Notice Period: Immediate Joiner

Key Responsibilities:

• 8+ years of experience in ETL/data testing.
• Design, implement, and execute data validation test plans and test cases.
• Understanding of data modelling and data governance principles.
• Experience with test automation frameworks and scripting (e.g., Python, Shell)Conduct thorough ETL testing, including data extraction, transformation, and loading.
• Validate data integrity across various sources and destinations (data lakes, warehouses, etc.)
• Perform data reconciliation and analysis to identify inconsistencies or data quality issues.
• Develop and maintain automated data testing frameworks using SQL or scripting languages.
• Strong experience with SQL and writing complex queries for data validation.
• Knowledge of data warehouse concepts and testing tools. Experience with ETL tools (e.g., Informatica, Talend, SSIS, etc.)
• Familiarity with cloud platforms (Azure, GCP) and modern data tools (e.g., Snowflake, Big Query).
• GCP is mandatory. Experience in Agile development and working within cross-functional teams.
• Exposure to BI tools (Power BI, Tableau, Looker)
• Familiarity with CI/CD pipelines and version control systems like Git ISTQB or equivalent testing certifications.

Job Description:

Position: Senior Data Engineer

Location: Chennai / Pune / Bangalore / Hyderabad

Working Type: WFO

Shift: UK Shift (2:00 – 11:00 PM)

Experience : 7+ years overall

Interviews: Assessment || 2 Interview rounds.

Notice Period: Immediate Joiner

Key Responsibilities

Implement ingestion, transformation, and optimization of enterprise data sources into Microsoft Fabric Lakehouse environments.

Configure and optimize Fivetran connectors (Oracle, SQL DB, etc.)

Manage large-volume ingestion and backfill operations

Implement Bronze to Silver transformation pipelines

Develop incremental load and CDC logic

Optimize Lakehouse performance and storage patterns

Implement monitoring (record counts, load duration, failure tracking)

Support Dev/Test/Prod promotion processes

Required Qualifications

7+ years of data engineering experience

Hands-on experience with Microsoft Fabric or Azure Synapse/Data Factory

Strong experience with Fivetran or similar ELT tools

Experience handling high-volume datasets (hundreds of millions of records)

Proficiency in SQL, Python, and data modeling concepts

Strong understanding of Medallion architecture.

Skills Referential (Required knowledge, skills and abilities)

Technical Skills:

Python

Pyspark

SQL

ETL Aws, Azure, gcp

Must have experience in Java

Must have experience in Spark

Must have experience in ETL coding

Strong expertise in coding

About AuxoAI:

AuxoAI is a global platform-based services firm. We help companies—turn their strategies into practical digital and AI solutions. By understanding how our clients make decisions, we use digital and Artificial Intelligence (AI) technologies to drive growth, enhance their operations, improve customer experiences, and provide clear, actionable insights from their data. What We Do We work across various industries such as healthcare, high-tech, consumer packaged goods (CPG), finance etc., and in sales, marketing, and customer support functions.

We help our clients with accelerating their digital and AI journeys through:

• AI Application Development

• Data, Digital and Cloud acceleration using AI

• AI Native Product Engineering

We are seeking a skilled and experienced Data Engineer to join our dynamic team. The ideal candidate will have 6+ years of prior experience in data engineering, with a strong background in AWS (Amazon Web Services) technologies. This role offers an exciting opportunity to work on diverse projects, collaborating with cross-functional teams to design, build, and optimize data pipelines and infrastructure.

Responsibilities:

* Design, develop, and maintain scalable data pipelines and ETL processes leveraging AWS services such as S3, Glue, EMR, Lambda, and Redshift.

* Collaborate with data scientists and analysts to understand data requirements and implement solutions that support analytics and machine learning initiatives.

* Optimize data storage and retrieval mechanisms to ensure performance, reliability, and cost-effectiveness.

* Implement data governance and security best practices to ensure compliance and data integrity.

* Troubleshoot and debug data pipeline issues, providing timely resolution and proactive monitoring.

* Stay abreast of emerging technologies and industry trends, recommending innovative solutions to enhance data engineering capabilities.

Requirements :

* Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

* 6+ years of prior experience in data engineering, with a focus on designing and building data pipelines.

* Proficiency in AWS services, particularly S3, Glue, EMR, Lambda, and Redshift.

* Strong programming skills in languages such as Python, Java, or Scala.

* Experience with SQL and NoSQL databases, data warehousing concepts, and big data technologies.

* Familiarity with containerization technologies (e.g., Docker, Kubernetes) and orchestration tools (e.g., Apache Airflow) is a plus.

Design, build, and maintain end-to-end data pipelines to ingest, process, and transform data from files, streams,

APIs, and relational/non-relational databases into Snowflake.   Develop and optimize ELT/ETL pipelines using Snowflake SQL,

Snowpipe, Streams & Tasks, and cloud-native orchestration tools.   Implement scalable data models and schemas (staging, curated, and consumption layers) to support analytics and reporting use cases.   Develop transformations and business logic using SQL and Python, including Snowflake UDFs and stored procedures.   Optimize Snowflake performance and cost through query tuning, warehouse sizing, clustering, and resource management.   Integrate Snowflake with cloud storage and services across AWS and Azure (e.g., object storage, data integration, and mess

Job Title: Data Engineer – PySpark | Oracle | GCP

Experience: 5–7 Years

Location: Hyderabad

Notice Period: Immediate Joiners Preferred

Job Summary

We are seeking an experienced Data Engineer with strong expertise in PySpark, Oracle, and Google Cloud Platform (GCP) to design, develop, and optimize scalable data pipelines. The ideal candidate should have hands-on experience in ETL development, data integration, and cloud-based data engineering solutions.

Key Responsibilities

• Design, develop, and maintain scalable ETL/data pipelines using PySpark.
• Extract, transform, and load data from Oracle databases into GCP environments.
• Build and optimize batch data processing workflows for high performance and reliability.
• Develop data engineering solutions using GCP services.
• Ensure data quality through validation, monitoring, and troubleshooting.
• Optimize SQL queries and ETL jobs for performance and scalability.

Required Skills

• 5–7 years of experience as a Data Engineer.
• Strong hands-on experience with PySpark.
• Solid experience with Oracle Database and advanced SQL.
• Hands-on experience with Google Cloud Platform (GCP).
• Strong understanding of ETL processes and data warehousing concepts.

Work Location: Hyderabad

Notice Period: Immediate Joiners Preferred

Title                                 : Snowflake Cortex AI Engineer

Experience                    : 5+ years

Location                         : Remote

Work type                      : Remote

Employment Type      : Full Time

Notice Period               : Immediate

Work Day                      : Mon to Fri

About the Role:

We are seeking a highly skilled Snowflake Cortex AI Engineer with 5+ years of experience in Data Engineering, AI, and Snowflake. The ideal candidate will have hands-on expertise in Snowflake Cortex AI, Snowpark, and Generative AI capabilities to build intelligent, scalable, and secure AI-powered data applications. The role involves designing AI-driven solutions, integrating LLM capabilities into enterprise workflows, and collaborating with cross-functional teams to deliver business value.

Key Responsibilities:

• Design, develop, and implement AI-powered solutions using Snowflake Cortex AI.
• Build intelligent data applications leveraging Snowpark, Cortex AI functions, and SQL.
• Develop Retrieval-Augmented Generation (RAG) solutions using enterprise data stored in Snowflake.
• Integrate Large Language Models (LLMs) into enterprise applications using Snowflake Cortex.
• Design and optimize AI workflows for document summarization, sentiment analysis, classification, translation, question answering, and text generation.
• Develop scalable data pipelines to support AI and machine learning workloads.
• Collaborate with Data Engineers, Data Scientists, and business stakeholders to understand AI use cases and deliver effective solutions.

Ensure AI solutions comply with enterprise security, governance, and data privacy standards.

• Optimize Snowflake performance and AI workloads for scalability and cost efficiency.
• Participate in architecture discussions, code reviews, and technical documentation.

Required Skills & Experience

• 5+ years of experience in Data Engineering, AI/ML, or Analytics.
• Strong hands-on experience with Snowflake.
• Experience working with Snowflake Cortex AI capabilities.
• Strong understanding of Snowpark (Python or SQL).
• Experience building AI-powered applications using enterprise data.
• Hands-on experience with Python and SQL.
• Knowledge of Generative AI, Prompt Engineering, and Large Language Models (LLMs).
• Experience designing and implementing RAG (Retrieval-Augmented Generation) solutions.
• Strong understanding of data modeling and data warehousing concepts.
• Experience developing and optimizing ETL/ELT pipelines.
• Experience integrating REST APIs and external AI services. 

Technical Skills

• Snowflake
• Snowflake Cortex AI
• Snowpark
• SnowSQL
• Snowpipe
• Streams & Tasks
• Secure Data Sharing
• Performance Optimization
• Role-Based Access Control (RBAC) 

Programming

• Python
• SQL
• AI & Machine Learning
• Generative AI
• Large Language Models (LLMs)
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• AI Model Integration
• Text Analytics
• Semantic Search
• Data Engineering
• ETL/ELT Development
• Data Warehousing
• Data Pipelines
• Structured & Semi-Structured Data Processing
• Cloud (Preferred)
• AWS / Azure / GCP

Preferred Skills

• Experience with vector search and semantic search concepts.
• Knowledge of Snowflake Cortex Analyst, Cortex Search, or Cortex Agents.
• Experience with AI governance and responsible AI practices.
• Familiarity with LangChain, LangGraph, or similar AI orchestration frameworks.
• Exposure to ML model deployment and MLOps practices.
• SnowPro certification is an added advantage. 

Educational Qualification

• Bachelor's or Master's degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, Engineering, or a related field.

Key Competencies

• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder management abilities.
• Ability to translate business requirements into AI-driven solutions.
• Strong collaboration and teamwork skills.
• Self-driven with the ability to work independently in a remote environment.

Job Title : Data Platform Engineer (SDE 2 / SDE 3)

Experience : 3 to 8 Years (SDE2 : 3 to 5 Years | SDE3 : 5.5 to 8 Years)

Location : Remote (Contract) → Pune (Post Conversion)

Employment Type : Contract-to-Hire (3 Months)

Mandatory Skills :

Java / Python / Go, SQL, Data Engineering, ETL / ELT, Apache Airflow, Apache Spark / Flink, Kafka, AWS/GCP/Azure, Distributed Systems, API Development, Docker, Kubernetes, CI/CD, System Design

Role Overview :

We are looking for a Data Platform Engineer with strong backend engineering expertise to build scalable, high-performance data platforms and distributed systems. The ideal candidate should have hands-on experience in developing production-grade backend applications along with designing and maintaining modern data pipelines.

Key Responsibilities :

• Build and maintain scalable ETL/ELT and data processing pipelines.
• Develop backend services and APIs using Java (preferred), Python, or Go.
• Design batch and real-time data pipelines using Spark, Flink, Kafka, and Airflow.
• Optimize SQL queries, data models, and distributed data systems.
• Work with cloud platforms (AWS, GCP, or Azure) and container technologies (Docker, Kubernetes).
• Implement CI/CD, monitoring, logging, and performance optimization.
• Collaborate with product and engineering teams on scalable system design and architecture.

Required Qualifications :

• SDE 2 : 3 to 5 years of Backend + Data Engineering experience.
• SDE 3 : 5.5 to 8 years of Backend + Data Engineering experience.
• Strong coding skills in Java (preferred), Python, or Go.
• Excellent SQL and data modeling knowledge.
• Hands-on experience with Airflow, Apache Spark / Apache Flink, or similar technologies.
• Experience building scalable backend services and APIs.
• Good understanding of distributed systems, system design. and scalable architecture.
• Experience with cloud platforms (AWS, Azure, or GCP).
• Hands-on experience with Docker and Kubernetes.
• Strong understanding of CI/CD pipelines.
• Excellent problem-solving and debugging skills.

Good to Have :

• Experience with Snowflake, BigQuery, or Redshift.
• Understanding of Kafka, Kinesis, or Pub/Sub
• Performance optimization of large-scale distributed systems
• FinTech or high-scale distributed systems experience.
• Knowledge of data governance, security, and compliance.

Preferred Candidate Profile :

• Strong Backend + Data Engineering experience
• Experience building scalable production systems
• Strong ownership mindset
• Good system design knowledge
• Experience processing millions of events using Kafka/Spark
• Built APIs supporting data infrastructure
• Production engineering experience

Interview Process :

1. Take-home Coding Assignment (48 Hours)
2. Leadership & Strategy Round (1 Hour)
3. Technical Depth – Data Engineering & Performance (1 Hour)
4. Culture & Values Fit (30 Minutes)

We are looking for a Data Engineer with at least 1 year of hands-on experience building solutions on Snowflake. The candidate should be comfortable designing, building, and managing reliable data pipelines that move data from multiple sources into a central data platform.

Responsibilities

• Build and maintain data pipelines for ingesting, transforming, and loading data into Snowflake
• Design scalable data models, schemas, tables, and views in Snowflake
• Develop ETL/ELT workflows using SQL, Python, or data orchestration tools
• Integrate data from APIs, databases, files, and third-party platforms
• Monitor pipeline performance, failures, data quality, and freshness
• Optimize Snowflake queries, warehouses, storage, and compute usage
• Implement incremental loads, change data capture, and scheduled workflows
• Work with engineering and business teams to understand data requirements
• Maintain documentation for pipelines, datasets, and data transformations

Requirements

• 1+ year of hands-on experience working with Snowflake
• Strong SQL skills and experience writing complex queries
• Experience building and managing ETL or ELT data pipelines
• Knowledge of data warehousing concepts, dimensional modelling, and data quality
• Experience with Python or another scripting language
• Familiarity with orchestration tools such as Airflow, Dagster, Prefect, dbt, or similar
• Understanding of APIs, relational databases, file formats, and cloud storage
• Ability to troubleshoot pipeline failures and performance issues
• Strong analytical, problem-solving, and communication skills

Good to Have

• Experience with dbt and Snowflake Tasks, Streams, Snowpipe, or Dynamic Tables
• Knowledge of AWS, Azure, or Google Cloud
• Experience with Kafka or other streaming platforms
• Familiarity with CI/CD, Git, monitoring, and data governance practices
• Experience integrating ERP, finance, or operational systems

Role Summary

We are hiring a Data Engineer / ML Data Pipeline Engineer to build and operate the data backbone of the Enterprise AI platform:

What You'll Own

• Ingestion & ETL/ELT pipelines for heterogeneous project folders (PDF drawings, SVG files, IFC models, BBS.json bar-bending-schedule data, Excel exports, and AI agent output JSON).
• AWS-based data architecture: S3 raw/staging/curated/outputs structuring, partitioning, versioning, and lifecycle management; querying via Athena/Glue and warehousing via Redshift or Snowflake as needed.
• Data validation frameworks: GUID cross-referencing between SVG and BBS data, schema enforcement, duplicate/orphan detection, reference integrity checks, and structured validation reporting.
• Agent run logging & observability: designing the database schema and pipelines that track every AI agent run (inputs, outputs, status, errors, cost, retries, reviewer feedback).
• AI Factory monitoring dashboards: operational dashboards (failure rates, retries, latency, data quality) and business dashboards (throughput, cost per run, rework rate) for Power BI/QuickSight or equivalent.
• ML data pipeline support: dataset preparation, labeling/annotation workflows, human-in-the-loop review tooling, and dataset versioning for models that classify or QC drawing issues.
• APIs: designing and building FastAPI/Flask endpoints to trigger validation runs and expose agent processing status to internal tools.
• Data quality & testing discipline: idempotent pipelines, quarantine/reject handling, regression and reconciliation testing, and root-cause debugging when pipelines or query performance degrade in production.

Key Skills — Non-Negotiable (Must-Have, Strong Level)

• Python — production-grade scripting: file/folder handling, JSON/schema processing, clean error handling, not just notebook-level scripting.
• SQL — strong hands-on ability, including GROUP BY/HAVING for duplicate detection, window functions, and daily aggregate/rate calculations (e.g., success-rate queries).
• AWS S3 data handling — practical experience structuring buckets for raw/staging/curated data, versioning, and avoiding overwrite issues at scale.
• Data validation — demonstrable experience building validation logic (set comparisons, duplicate/missing detection, structured pass/fail reporting), not just "I write assertions."
• ETL/ELT pipeline design — end-to-end ownership of at least one pipeline: source → transform → storage → validation → monitoring → business outcome, with clear articulation of what they personally built.
• Query/warehouse engine judgment — working knowledge of when to use Athena vs. Redshift vs. Snowflake (or equivalent), partitioning, clustering, sort/distribution keys, and storage format trade-offs (Parquet vs. JSON vs. CSV).

Key Skills — Good to Have

• Dashboarding — Power BI / QuickSight (or equivalent) fact/dimension table design, KPI cards, drill-downs; medium-to-strong level is a plus but trainable.
• FastAPI / Flask — building real endpoints with request/response schemas and basic error handling; especially valuable for validation-trigger and agent-status APIs.
• ML data pipeline experience — dataset labeling, annotation platform design, train/test/validation splitting, dataset versioning; strong on the pipeline/data side rather than model training itself.
• Human-in-the-loop / review tooling — experience building or contributing to browser-based labeling/review platforms (session persistence, label schema, export formats).
• Large-scale metadata querying — experience making file discovery fast across large volumes (1,000+ projects, thousands of files each) via metadata index tables, event-based ingestion, or catalog tools like AWS Glue.

Key Responsibilities

Data Modeling & Power BI Development

Design, develop, and maintain high-performance Power BI dashboards and reports.

Build robust, scalable data models using **advanced DAX (Data Analysis Expressions)** to implement complex business logic, time-intelligence calculations

Optimize existing reports and data models for speed, efficiency, and scalability.

Advanced Data Extraction & Transformation

Write, optimize, and debug **complex SQL queries** (including subqueries, CTEs, window functions, and advanced joins) to extract data from various enterprise data warehouses.

Ensure data integrity and consistency between source systems and front-end reports.

Required Technical Skills & Qualifications

Must-Haves (Non-Negotiable)

Power BI & DAX: Minimum of 1–3 years of hands-on experience building Power BI solutions. Deep understanding of evaluation contexts (Filter Context vs. Row Context) and complex DAX functions (CALCULATE, FILTER, ALL, EARLIER, and time-intelligence).

Expert SQL Skills: High proficiency in writing complex, optimized SQL queries. Must be comfortable handling large datasets, writing Common Table Expressions (CTEs), utilizing window functions (RANK, LEAD/LAG, PARTITION BY), and analyzing query performance.

Nice-to-Haves

Data Modeling: Strong understanding of relational database concepts, star/snowflake schemas, and data normalization/denormalization.

Familiarity with cloud data platforms like Snowflake, Azure Synapse, or AWS Redshift.

Basic knowledge of Python for data manipulation.

Soft Skills & Core Competencies

Problem-Solving: A natural curiosity to dig into data anomalies and find the root cause of discrepancies.

Communication: Ability to explain complex technical data constraints to non-technical business users clearly.

Attention to Detail: Precision in data validation to ensure business decisions are made on 100% accurate reporting.

Job Title: 

Role Overview

We are seeking a high-performing Senior Power BI Analyst / Developer to design, build, and optimize our next-generation enterprise analytics platform. In this role, you will be the core architect of our data visualization layer, turning complex data streams into actionable executive insights.

The ideal candidate bridges the gap between traditional business intelligence and modern cloud data engineering. You will be responsible for building highly performant, scalable data models on Azure and creating seamless, near real-time reporting solutions utilizing streaming data and Change Data Capture (CDC) pipelines.

Key Responsibilities

1. Advanced Data Modeling & Performance Tuning

Design, implement, and maintain enterprise-grade, highly scalable data models (Star and Snowflake schemas) within Power BI and Azure Analysis Services/Fabric.

Optimize complex DAX queries, M code, and data loading processes to ensure sub-second dashboard responsiveness on large-scale datasets.

Implement advanced performance-tuning strategies, including aggregations, incremental refreshing, and hybrid tables (combining Import and DirectQuery).

2. Streaming Analytics & Real-Time Reporting

Architect and implement near real-time dashboards utilizing Power BI streaming datasets, push datasets, and automatic page refreshes.

Integrate reporting front-ends with modern Change Data Capture (CDC) architectures to surface low-latency transactional updates.

Partner with data engineering teams to ingest and model fast-moving event data coming from cloud streams into analytical reporting structures.

3. Azure Architecture Integration

Leverage the Azure data stack (Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure Databricks, and Azure SQL DB) to build robust downstream reporting layers.

Participate in the orchestration of data pipelines (using Azure Data Factory or Synapse pipelines) to align with dashboard refresh schedules.

Embrace modern Power BI developer workflows using Tabular Editor, ALM Toolkit, DAX Studio, and Git integration for version control.

Required Technical Skills & Qualifications

Experience: 3-5+ years of dedicated experience developing, modeling, and tuning enterprise Power BI solutions, with at least 2+ years operating within an Azure cloud ecosystem.

Expert DAX & Power Query: Mastery of complex DAX computations (time-intelligence, advanced filtering) and optimized M-code data transformations.

Modern Data Paradigms: Proven experience handling Streaming Data or CDC (Change Data Capture) patterns (e.g., streaming from Debezium, Azure Event Hubs, Kafka, or Synapse Link) into Power BI.

Azure Stack Expertise: Solid working knowledge of Azure data resources (Synapse, Data Factory, Azure SQL, or Azure Fabric Lakehouses).

Data Engineering Concepts: Deep understanding of data warehousing concepts, including slowly changing dimensions (SCDs), partitioning, and indexing strategies.

Education: Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related quantitative field (or equivalent practical experience).

Preferred (Nice-to-Have) Skills

Microsoft Certifications: PL-300 (Power BI Data Analyst) or DP-600 (Fabric Analytics Engineer).

Experience with CI/CD deployment pipelines for Power BI deployment pipelines or Azure DevOps.

Familiarity with SQL performance tuning (analyzing execution plans, optimizing views, and indexing).