Top Data Engineer Jobs in Chennai

Design, build, and maintain data pipelines and ETL processes; ensure data quality and availability; collaborate with cross-functional teams to support analytics and reporting; implement data solutions and support data platform operations.

Design, develop, and maintain data platforms and ETL processes on cloud-based big data systems. Own architecture, write code and APIs, build automation and CI/CD pipelines, collaborate with product managers, perform code reviews, and support data integration, modeling, and BI solutions for global customers.

Lead database design, data modeling, migrations, and schema evolution across MySQL/Postgres and GCP databases. Design and operate config-driven ETL/ELT pipelines and streaming ingestion, enable CI/CD, troubleshoot data flows, and mentor offshore DB/ETL engineers.

Design and build scalable Azure-based data models, ETL pipelines, and data architectures. Implement data integration using Azure Data Factory/Databricks, ensure data quality, governance, security, and collaborate with data scientists and DevOps for deployment and CI/CD.

Design, develop, and maintain MSSQL and MySQL databases; write SQL/PLSQL, stored procedures, triggers, cursors, and schedulers. Troubleshoot jobs and performance, work with ETL tools (Talend/Fivetran/Pentaho) and document-oriented databases, support cloud database administration, enforce security, collaborate with developers/QA/DevOps, produce technical documentation, and potentially lead small projects.

Designs and operates enterprise data governance infrastructure across Snowflake and AWS. Responsibilities include RBAC, authentication, masking, row-level security, compliance automation, Terraform provisioning, access workflows, monitoring, SOX evidence, data sharing, Iceberg governance, orchestration, cost optimization, and Snowflake AI/ML governance. The role also supports platform reliability, self-service data access, and scalable identity integration using Okta and SCIM.

Design, build, and maintain scalable ETL/ELT pipelines and data models using SQL and Python. Optimize database and pipeline performance, enforce data quality and governance, and collaborate with stakeholders to deliver analytics-ready datasets and data products.

As a Senior Data Engineer, you will develop and maintain data pipelines for healthcare data, ensuring data quality, reliability, and adherence to enterprise standards while collaborating with architects and stakeholders.

The Senior Data Engineer will develop data ingestion, transformation, and enrichment pipelines, ensuring data quality and reliability. Responsibilities include creating Bronze and Silver data layers, using SQL and dbt for implementation, and collaborating with architects and stakeholders to align data solutions with enterprise standards.

As an Azure Data Engineer, you will innovate and maintain data engineering services for Fortune clients, develop software for large projects, and utilize Azure components for solutions.

Designs and leads enterprise business intelligence and analytics solutions using Power BI, Microsoft Fabric, Snowflake, Azure Data Factory, and modern data architectures. Responsibilities include developing semantic models, datasets, dashboards, data pipelines, data products, and scalable data platforms; implementing governance, quality, lineage, and metadata practices; enabling self-service analytics; optimizing performance; evaluating emerging tools such as Promethium; and advising stakeholders while mentoring data teams.

Designs and maintains enterprise data architecture, including conceptual, logical, and physical data models. Establishes data standards, metadata conventions, governance policies, security frameworks, and data dictionaries. Leads Master Data Management initiatives to improve consistency and integrity across platforms. Collaborates with business, analytics, application, and engineering teams, while supporting database, data warehouse, integration, and ETL pipeline efforts aligned with business, regulatory, security, and performance requirements.

Build, optimize, and maintain scalable AWS-based data pipelines and data lakes (Snowflake/Databricks). Implement streaming ingestion (Kinesis/Kafka), CDC, automated data quality checks, and ML-ready feature stores. Collaborate with Product, ML, and Analytics teams, own pipeline QA, observability, and participate in on-call rotations to resolve production data issues.

Build, monitor, optimize, and maintain ETL/ELT pipelines using Google Cloud technologies. Develop complex SQL transformations, design dimensional data models, improve query performance and cloud cost efficiency, and ensure data integrity. Collaborate with analysts and stakeholders to translate SaaS and insurance metrics into technical solutions. Participate in code reviews, documentation, troubleshooting, and data warehouse lifecycle management while supporting US business hours.

Design, build, and optimize scalable GCP data pipelines and BigQuery data warehouses. Develop ETL/ELT workflows, implement data modeling, ensure data quality, optimize query performance and costs, maintain orchestration with Cloud Composer/Dataflow/Pub‑Sub, and automate deployments with CI/CD while enforcing security and governance.

The Data Engineer will create and maintain data pipelines, optimize data delivery, build analytics tools, and support cross-functional teams with data infrastructure needs.

Develop and maintain reliable data pipelines, ELT processes, workflow orchestration, custom data connectors, and CI/CD pipelines using Airflow, Python, PySpark, Hive/Trino, SQL, and Snowflake. Collaborate with cross-functional teams, monitor and troubleshoot data workflows, validate and test data, document technical processes, and support data governance.

Leads end-to-end data products from ingestion and transformation through semantic modeling, Power BI reporting, documentation, governance, and user adoption. Builds Microsoft Fabric pipelines, Lakehouse or Warehouse architectures, dimensional models, and secure, optimized Power BI solutions. Analyzes data, supports advanced analytics and AI-assisted reporting, establishes quality and CI/CD practices, collaborates with cross-functional stakeholders, and manages data engineers through objectives, reviews, and development.

Design, deploy, and scale production-grade machine learning, deep learning, and generative AI solutions on cloud platforms, primarily Azure. Lead LLM applications such as chatbots and agentic workflows, oversee the full ML lifecycle, ensure production reliability and governance, and partner with global business and technical stakeholders. Mentor junior team members, influence architecture and best practices, and drive AI initiatives from problem definition through measurable business impact.

Design, build, and maintain scalable ETL/data pipelines and data management blueprints. Ingest, transform, and curate large complex datasets for analytics, support cloud (AWS) implementations, enable downstream consumers, and provide technical leadership and operational support to ensure data governance, quality, and automation.

Design and develop scalable data-driven solutions and pipelines using Python and SQL. Leverage Azure data services to build secure, high-performance architectures, ensuring data integrity, governance, and overall success of data projects.

Design and build scalable data pipelines and architectures on Azure using ADF, Databricks, and Synapse. Implement Kimball/Data Vault modeling, write ETL in Python and T-SQL, ensure data governance (Azure Purview), automate tests, troubleshoot issues, document processes, and collaborate across teams.

Design and develop data-driven solutions and scalable data pipelines using Python and SQL. Leverage Azure data products to build robust, secure architectures, ensuring data integrity, performance, and governance while supporting data-centric client projects.

As a hands-on data engineer, you'll build and enhance scalable data platforms, develop data pipelines, and collaborate with various teams to improve data solutions and system performance.

Serve as data steward and subject-matter expert for a mobile banking platform. Design and guide implementation of data collection, storage, retrieval, message content/metadata, and data-management best practices. Maintain and improve data quality, guide report development, and provide data-driven insights on customers, usage, and performance.