Top Data Engineer Jobs in Chennai

Provide L1-L3 production support for enterprise MongoDB Atlas environments, including monitoring, scaling, backups, upgrades, performance tuning, security, networking, disaster recovery, and incident resolution. Build Terraform-based infrastructure automation, support multi-cloud deployments across Azure, AWS, and GCP, partner with application and engineering teams, coordinate vendor escalations, and participate in 24/7 on-call coverage.

Analyze business and user requirements across financial services value streams, define technical requirements, develop cloud solution options, and manage business and IT stakeholders. The role requires understanding existing framework capabilities, complex data models, cross-functional solution design, clear documentation, team coordination across regions, and familiarity with the SDLC from design through testing and implementation.

Design, implement, and administer SAP Analytics Cloud (SAC) planning and reporting solutions. Lead SAC modelling, story creation, SAC admin, and migrations from BPC to SAC. Collaborate with data, product, and functional teams to define data acquisitions, virtual models in HANA, pipelines, and security. Provide support, training, and stabilization efforts while managing multiple projects and mentoring team members.

The Data Engineer role supports NIQ’s consumer intelligence and analytics environment. The provided description contains only company information, benefits, diversity commitments, and recruitment-process disclosures; it does not specify day-to-day responsibilities, required qualifications, technologies, compensation, travel, or clearance requirements.

Build, maintain, test, and optimize scalable data architectures and platforms. Extract, transform, and load data for analysts and data scientists; source and integrate new data; apply DevOps practices; resolve technical issues; and develop secure, cost-effective data solutions using AI. Collaborate with technology, architecture, and business stakeholders while improving data quality, structures, metrics, and customer-focused outcomes.

Build and support data integration and real-time streaming platforms. Responsibilities include data modeling, transformation, cloud-based data engineering, CI/CD and DevOps practices, Kafka and CDC solutions, topic and schema management, connector and stream-processing pipelines, legacy-to-modern platform integration, data mapping, quality management, and stakeholder collaboration.

Designs, develops, and maintains scalable data solutions, pipelines, and ETL processes. Ensures data quality, supports data migration across systems, monitors and optimizes workflows, troubleshoots data issues, documents architecture, and collaborates with cross-functional teams. The role requires independent execution, subject matter expertise, and mentoring junior team members, with a focus on PySpark and large-scale distributed data processing.

Design and maintain scalable pipelines for visual and voice data, including ingestion, processing, validation, testing, observability, and automation. Build cloud, on-premise, and containerized data infrastructure; optimize storage, retrieval, and caching for large media assets; debug complex data issues; and collaborate with data scientists, operators, and product teams to support machine learning workflows.

Engineer and maintain complex, secure, high-performance software, scripts, and tools across the full development lifecycle. Responsibilities include requirements analysis, architecture, coding, testing, deployment, operations, maintenance, and decommissioning. The role collaborates with engineers, architects, business analysts, and stakeholders while applying best practices for scalability, automation, virtualization, availability, optimization, and performance within an Agile, DevOps environment.

Build scalable data pipelines, models, ETL processes, automation, streaming ingestion, and data architecture solutions. Design and integrate production-grade AI agents using MCP, APIs, microservices, RAG, and enterprise data platforms. Establish cloud infrastructure, security, observability, testing, governance, and cost optimization practices while providing technical leadership and engaging stakeholders to deliver customer and commercial value.

Lead the design and delivery of scalable Lakehouse data solutions using Azure Databricks, PySpark, SQL, Spark, and Delta Lake. Own enterprise ETL/ELT pipelines, data modeling, migration, performance optimization, data quality, governance, security, and monitoring. Define engineering standards, review technical designs, collaborate with architects and stakeholders, support planning and risk management, and mentor Data Engineers.

Build and maintain high-performance data infrastructure and real-time pipelines for cloud-based autonomous systems. Design, implement, document, unit-test, deploy, and integrate data engineering components. Develop CI/CD workflows, troubleshoot systems, monitor AWS environments, estimate development work, and collaborate with stakeholders while ensuring secure, optimized, high-quality deliverables.

Designs, builds, maintains, and optimizes data pipelines, ETL processes, data warehouses, and data lakes. Collaborates with data scientists, analysts, software engineers, and stakeholders to develop scalable data models and infrastructure. Responsibilities include ensuring data quality, security, governance, reliability, and performance; testing and troubleshooting pipelines; documenting systems; and evaluating emerging data engineering technologies.

Build and maintain batch and streaming data pipelines across AWS, Azure, and GCP using Databricks, Spark, PySpark, SQL, and Delta Lake. Implement medallion architecture, CDC, SCD, data quality, governance, security, metadata, and lineage controls. Configure storage, orchestration, CI/CD, monitoring, troubleshooting, and performance optimization. Collaborate with architects, data scientists, engineers, analysts, and product teams while documenting pipelines, transformations, testing, and operational procedures.

Design, build, and maintain scalable data pipelines and ETL/ELT workflows using Azure Data Factory, Databricks, PySpark, Python, and SQL. Transform large datasets, optimize data ingestion and queries, monitor pipeline performance, resolve data quality issues, support data modeling, document engineering standards, and contribute to cloud data infrastructure improvements. The role also involves collaboration with architects and senior engineers and requires familiarity with Azure Synapse, Delta Lake, governance, and security.

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.

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.

Principal Data Engineer responsible for developing and supporting Databricks pipelines, integrating heterogeneous healthcare data sources, modeling unified data products, and ensuring data quality and availability. The role partners with business stakeholders, product teams, data engineers, and clinical leaders to gather requirements, create datasets, support ad hoc analytics, enable machine learning and AI use cases, and assist with acquisition integrations. Extensive healthcare data, cloud, SQL, PySpark, and Python experience is required.

Design, develop, and maintain scalable ETL pipelines and data infrastructure for large-scale structured and unstructured datasets. Integrate and govern data across sources, optimize pipeline performance, and deploy cloud-based solutions. Collaborate with data scientists, analysts, and engineers to deliver accessible datasets. Implement automation, testing, security, compliance, monitoring, and continuous improvements across data engineering workflows.

Senior data engineer responsible for designing, building, and optimizing scalable cloud-based data pipelines and lakehouse solutions on AWS. Lead complex integrations, implement data modeling and governance, promote engineering best practices, automate infrastructure and CI/CD, mentor engineers, and collaborate with stakeholders to deliver reliable analytics and BI solutions.

Design, build, and maintain AWS-native cloud data pipelines and ETL/ELT processes (S3, Glue, Redshift, AppFlow, Step Functions). Integrate enterprise apps (Salesforce, NetSuite), develop scalable data models, monitor pipeline performance, ensure data quality and governance, support CI/CD and IaC (Terraform), apply AI/ML for data quality/monitoring, and collaborate with stakeholders and peers.

Build and maintain scalable data products using Snowflake, dbt, and Airflow. Responsibilities include dimensional data modeling, SQL transformations, pipeline orchestration, data quality testing, exploratory analysis, SLA monitoring, documentation, and stakeholder requirement gathering. Partner with analytics, product, engineering, and business teams to deliver reliable, production-ready datasets while following software engineering, CI/CD, testing, and deployment practices.

Design, build, and maintain scalable data pipelines, ETL/ELT processes, data warehouses, data lakes, and analytics infrastructure on GCP. Develop data models and schemas for marketing attribution and campaign analytics, while ensuring data quality, security, privacy, governance, and regulatory compliance. Implement monitoring, alerting, troubleshooting, performance tuning, and capacity planning. Collaborate with data scientists, analysts, product teams, and business stakeholders to deliver reliable marketing data solutions.

Lead data engineering for enterprise banking platforms, building ETL/ELT pipelines, Oracle and SQL/PLSQL solutions, data warehouses, and risk and regulatory reporting systems. Provide production support, troubleshoot batch and data issues, automate validation and reconciliation, ensure data quality and governance, and collaborate with Risk, Finance, Compliance, and Technology stakeholders. Mentor team members and support regulatory submissions and audit readiness.

Build and maintain Azure-based data pipelines for governance and analytics. Develop Databricks ETL/ELT workflows using PySpark, SQL, and Delta Lake; automate data quality, lineage, monitoring, access auditing, cost analysis, retention tracking, policy enforcement, and metadata integration with Microsoft Purview. Maintain curated governance metric tables, optimize performance, troubleshoot pipeline issues, and collaborate with engineering, QA, and reporting teams while meeting security and privacy standards.