Top Data Engineer Jobs in Chennai

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.

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.

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 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.

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.

Design, build, maintain, and optimize ETL/ELT data pipelines and warehouse schemas; implement data security, storage and processing optimizations; deliver cloud data solutions and production ML deployments; develop and tune complex SQL; lead cross-functional efforts and engage stakeholders. Domain expertise in supply chain and manufacturing data is required.

Leads enterprise data engineering, warehousing, analytics, reporting, and regulatory data initiatives for banking and financial services. Designs data platforms and pipelines, develops Oracle SQL and PL/SQL solutions, builds dashboards and reporting frameworks, and ensures data quality, governance, reconciliation, and lineage. Supports lending, risk, securities, tax, payments, treasury, and customer data management. Provides production support, incident resolution, root-cause analysis, and stakeholder collaboration across business, risk, finance, compliance, and technology teams.

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.

As a Data Engineer, you will design, develop, and maintain data integration solutions, optimize ETL/ELT pipelines, and support data workflows for analytics and reporting.

The Data Engineer role involves data management, system design, and collaboration with teams to implement effective data solutions.

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

Design, build, and operate ETL/ELT data pipelines, data models, REST APIs, and production AI/LLM applications. Develop AI skills, agent workflows, and MCP server integrations for internal systems and business use cases. Collaborate with product, data, and engineering teams while owning reliability, quality, documentation, and deployment. The role requires independent execution, strong communication, and experience with Python, SQL, data engineering, AI application development, and platform capabilities.

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, build, and maintain scalable ETL/ELT and real-time data pipelines on AWS. Manage Airflow DAGs, Kafka ingestion, Redshift optimization, data quality checks, API integrations, and cost optimization while collaborating with BI for reporting needs.

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 optimize enterprise-scale data pipelines and modern data platforms. Develop ETL/ELT workflows with Python, PySpark, Scala, SQL, Informatica, Databricks, and cloud services. Manage databases, orchestration, streaming, containers, CI/CD, governance, and multi-cloud environments. Collaborate with BI teams, troubleshoot performance issues, and maintain technical documentation.

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.