Top Data Engineer Jobs in Chennai

Design, build, and optimize scalable data pipelines and platforms (Snowflake, Databricks, Spark). Ensure data quality, lineage, and observability. Mentor junior engineers, lead cross-team initiatives, implement CI/CD, and improve internal data engineering tools and architectures.

Design, build, and optimize scalable data pipelines and structures for analytics and downstream applications. Implement SQL-based transformations, ingestion frameworks, and validation checks across cloud platforms (Snowflake, Databricks). Ensure data quality, lineage, governance, and operational reliability. Collaborate with stakeholders, mentor junior engineers, and improve internal data engineering tools, automation, and CI/CD processes.

Design, build, and maintain cloud-native data pipelines and ETL/ELT workflows for high-volume trade lifecycle data. Integrate DTCC sources (RTTM, Settlement Web) into a Unified Trade Record, process ISO 15022 messages, manage trade exception databases, optimize SQL Server performance, and ensure data quality, lineage, governance, and observability while collaborating with business stakeholders.

Design, develop, and maintain data solutions and pipelines. Implement ETL processes, ensure data quality, optimize pipeline performance, collaborate with cross-functional teams, and support data migration and cloud storage solutions to enable reliable data operations.

The Lead Data Engineer will oversee enterprise data engineering services, focusing on data pipeline management, technical strategy, governance, and operational excellence across various data platforms and tools.

Design, build, and maintain scalable ETL/ELT pipelines into Snowflake; develop and optimize SQL-based data warehouse solutions; create Power BI dashboards; ensure data quality, performance tuning, documentation, stakeholder collaboration, and governance for enterprise reporting.

Design, build, and maintain batch and real-time data pipelines; collaborate with architects, product owners, analysts, and data scientists; implement data integration, replication, and masking; optimize pipeline performance; apply engineering best practices (CI/CD, testing, monitoring); and support data platform technologies like Databricks, Snowflake, and cloud services.

Participate in design, development, testing, and implementation of application systems. Analyze complex problems, estimate feasibility/costs, recommend solutions, ensure security and compliance, support users, coach junior analysts, and contribute to project delivery within Technology/Applications Development.

Design, build, and maintain scalable ETL/ELT data pipelines and data platforms on AWS. Manage structured and unstructured data, optimize performance, implement security and governance, monitor pipelines, and collaborate with data science, analytics, and BI teams within Agile processes.

As a Data Engineer, you will innovate and maintain technology services, develop software systems for large projects, and work across all SDLC phases.

Lead the data team to build scalable data products, create data pipelines, partner with business teams, and ensure data quality while impacting revenue and decision-making.

Build, maintain, test and optimize scalable data architecture and pipelines. Extract, transform and load data for analysts and scientists, apply DevOps practices, collaborate with architecture teams, and ensure cost-effective, secure data solutions.

Design, build, test and optimise scalable data architectures and ETL pipelines. Extract, transform and load large-scale data for analysts and data scientists, apply DevOps practices, collaborate with stakeholders, source and integrate new data, and drive cost-effective, secure data solutions.

Design, implement, and maintain data pipelines using Elasticsearch and data warehousing solutions, integrate data sources, and ensure data accuracy while collaborating with cross-functional teams.

Design and build high-performance, real-time data pipelines and infrastructure for agentic AI. Implement, test, document, and deploy components using Python, SQL, AWS, Terraform and CI/CD. Provide estimates, own delivery, debug production issues, and optimize performance and cost while collaborating across teams.

Design, implement, test, and deploy real-time data pipelines and infrastructure on AWS. Build CI/CD pipelines, write clean Python code, support ingestion and data modeling, and ensure secure, documented, production-ready components while collaborating with stakeholders.

Lead architecture and implementation of GitLab's in-product insights and Data Marketplace. Design and coordinate ingestion, modeling, and synchronization into the Knowledge Graph; integrate external systems; publish observability data; define operational readiness; translate ambiguous product problems into technical plans; resolve cross-team dependencies; and mentor other engineers.

The role involves designing and building data systems using various technologies, especially within Microsoft Azure, and requires experience in system integration and data warehousing.

Design, build, and maintain scalable cloud-native data pipelines for trade lifecycle processing. Ingest, transform, and normalize high-volume financial data (DTCC, ISO15022), implement MQ/API-based ingestion, optimize SQL Server performance, maintain UTR schemas and trade exception databases, ensure data quality, lineage and governance, and collaborate with reconciliation, settlements, and operations stakeholders.

Design and build scalable cloud-native data pipelines to ingest, clean, transform, chunk, embed, index, and serve enterprise data for LLM applications. Optimize vector database architectures, embedding generation, and RAG pipelines, collaborate with ML and software teams, implement Airflow orchestration, and ensure pipeline performance, monitoring, and reliability on AWS.

Design, build, and maintain scalable Azure-based data pipelines and systems (Databricks, Data Lake, Synapse, ADF). Collaborate with cross-functional teams, ensure data quality and integrity, optimize performance, monitor and troubleshoot systems, and stay current with industry trends to improve solutions.

As a Data Engineer, you will build and maintain data systems for drug safety AI tools, ensuring reliable data processing and storage for research teams.

Design, build, and maintain scalable data pipelines and infrastructure (SQL warehouses, data lakes) using Azure and Databricks. Write production-grade PySpark/Python and SQL, consolidate and clean data, implement ETL/ELT, data modeling, orchestration/monitoring, and produce Power BI dashboards. Collaborate with stakeholders on data lineage and document processes for reproducibility.

The role involves enhancing application systems, providing solutions, consulting with clients, and analyzing vulnerabilities in applications while advising lower-level analysts and ensuring compliance with regulations.

The Senior Azure Data Engineer will design, develop, and maintain data infrastructure and pipelines, ensuring data quality, integrity, and alignment with business objectives. Responsibilities include collaborating with teams and establishing best practices for data engineering.