Top Data Engineer Jobs in Chennai

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Lead data science initiatives for Quvia’s connectivity platform by developing predictive, prescriptive, forecasting, classification, and optimization models. Integrate anomaly detection, knowledge graphs, neural networks, and generative AI into products; deploy and continuously refine production-ready models; establish data quality and governance standards; document workflows; and mentor junior data scientists while collaborating cross-functionally with engineering and product teams.

Design, develop, and support scalable GCP data ingestion and transformation pipelines (batch and streaming) using Dataflow, Dataform, BigQuery, Pub/Sub and GCS. Build ETL/ELT and CDC processes for lakehouse architectures, optimize BigQuery performance, implement monitoring, security/IAM, and collaborate with architects, governance, analytics, and business teams to enable reporting and AI-driven data products.

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.

Leads enterprise data engineering, analytics, reporting, regulatory data, and platform initiatives for banking and financial services. Designs data warehouses, lakes, pipelines, integrations, reporting marts, dashboards, and Oracle solutions. Ensures data quality, governance, reconciliation, lineage, and regulatory compliance across lending, risk, securities, tax, payments, treasury, and customer data. Provides L2/L3 production support, resolves incidents, drives performance improvements, and partners with senior stakeholders and global teams.

Lead the design, modernization, operation, and support of enterprise platforms across UNIX, Linux, Windows, cloud, and hybrid environments. Manage servers, virtual machines, storage, databases, workload scheduling, middleware, managed file transfers, reporting platforms, automation, security, monitoring, disaster recovery, and major incidents. Drive platform stability, resiliency, observability, compliance, performance, and operational excellence while partnering with infrastructure, application, data, cybersecurity, and business teams.

Leads enterprise data engineering, warehousing, integration, analytics, reporting, and regulatory data initiatives for banking and financial services. Designs scalable pipelines and data platforms, develops Oracle SQL and PL/SQL solutions, builds BI dashboards, manages data quality and governance, supports lending, risk, securities, tax, payments, treasury, and customer data domains, and provides L2/L3 production support. Partners with senior stakeholders and leads global teams to improve reporting accuracy, compliance, reconciliation, and operational performance.

Lead the design, modernization, operation, and support of enterprise platforms across UNIX, Linux, Windows, cloud, and hybrid environments. Responsibilities include infrastructure and virtualization management, workload scheduling with Tivoli Work Scheduler, Oracle and SQL support, managed file transfer, automation using Python, Shell, and PL/SQL, security and compliance, monitoring, incident management, disaster recovery, and BusinessObjects reporting support. The role drives platform stability, resiliency, observability, SLA performance, and operational excellence.

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.

Own the architecture, development, and operation of scalable data pipelines, distributed data systems, databases, and data products. Define data quality and reliability standards, optimize data models and SQL workloads, build reusable infrastructure, and establish testing, observability, and deployment practices. Partner with engineering, AI, data science, and product teams to deliver reliable data solutions, influence platform strategy, and mentor engineers.

Design, build, and maintain dbt models and Snowflake data warehouses, own CI/CD and orchestration for pipelines, implement data quality tests, partner with stakeholders to translate requirements into production-ready data models, and optimize performance and observability.

Design, develop and maintain data pipelines, optimize data architectures, and collaborate with data scientists while ensuring data quality and governance.

Design and implement data federation and lakehouse architectures (Starburst/Trino), build scalable ETL/ELT pipelines using Python and Spark, optimize Spark and federated queries, manage Delta/Iceberg/Hudi tables, and enforce governance, access control, and data masking for analytics and AI use.

Design and optimize scalable data platforms using Microsoft Fabric and Databricks; create data pipelines and models for enterprise analytics while collaborating with data science teams.

As a Data Engineer, you will build scalable ETL/ELT pipelines, optimize data structures, and develop impactful Power BI solutions for stakeholders.

The Data Engineer will design and maintain data models, develop BI solutions, integrate data, and optimize data flows while collaborating with stakeholders.

Build, orchestrate, enhance, and troubleshoot scalable data pipelines using Python, PySpark, SQL, and Apache Airflow. Integrate multiple data sources, optimize pipeline performance, conduct root cause analysis, and apply data modeling and warehousing techniques. Collaborate with stakeholders to meet data-processing needs and provide weekend or shift support when required.

Design and build a greenfield, multi-layer data warehouse and semantic metrics layer; separate analytics from OLTP; ensure data quality, traceability, tenant isolation, and high SLAs; enable linking of structured records to drone imagery/video; deliver self-service data access, observability, and infrastructure automation to scale across products and tenants.

The Senior Data Platform Engineer will architect and optimize Hexnode's data infrastructure for high-volume security telemetry and analytics, ensuring scalability, efficiency, and data security.

Own the architecture, scalability, governance, and technical direction of Amtech’s cloud data platform, including warehouses, tenant data stores, feature stores, and event pipelines. Lead multi-tenant data architecture, hybrid AI data routing, data security, observability, and reliability standards. Build data foundations for analytics, machine learning, and AI agents, including vector databases and RAG pipelines. Provide technical leadership through design reviews, mentoring, roadmap planning, and cross-functional architecture collaboration.

Design, develop, and support enterprise data platforms, ETL/ELT pipelines, Oracle databases, data warehouses, and risk and regulatory reporting solutions. Build automation, optimize SQL and data processing, ensure data quality, reconciliation, lineage, governance, and audit readiness. Provide L2/L3 production support, resolve incidents, manage batch operations, and perform root-cause analysis. Collaborate with Risk, Finance, Regulatory, Business, and Technology stakeholders while mentoring junior team members.

Design, develop, and support enterprise data platforms, ETL/ELT pipelines, Oracle and SQL/PLSQL solutions, data warehouses, and risk and regulatory reporting systems. Provide production support, troubleshoot batch and data issues, optimize performance, automate validation and reconciliation, and ensure data quality, governance, lineage, and audit readiness. Collaborate with Risk, Finance, Regulatory, Business, and Technology stakeholders while providing technical leadership and mentoring.

Migrate SQL Server data products and analytical workloads to a modern cloud platform. Redesign legacy SQL, ETL processes, and business logic; build scalable pipelines and analytical models; validate data quality; collaborate with stakeholders; and improve platform reliability, performance, cost efficiency, and AI-assisted data engineering automation.

The Senior Data Engineer will architect and implement cloud data platforms, leading a team to deliver high-performance solutions and collaborate with product teams on data transformation initiatives.

The role involves building and scaling the Acceldata Open Data Platform, focusing on observability, coding, debugging, and collaborating with teams and the open-source community.