Top Data Engineer Jobs in Chennai

Build production-grade agentic AI systems that reason, search, and execute workflows. Develop RAG pipelines, AI agents, semantic search layers, data processing solutions, dimensional data models, and scalable AI workloads. Work with structured and unstructured data, LLMs, vector databases, cloud infrastructure, CI/CD, and ML observability. Responsibilities include fine-tuning models, prompt engineering, optimizing context windows, and deploying reliable AI applications.

Designs and refines data platform blueprints, integrating systems and data models with support from architecture teams. Leads cross-functional coordination, makes team decisions, solves complex platform challenges, mentors team members, and evaluates emerging technologies. The role also requires expertise in Kinaxis, data integration, data modeling, cloud data platforms, governance, and security best practices.

Supports data platform architecture, blueprint development, integration, and performance troubleshooting. Collaborates with Integration and Data Architects to align systems, data models, and platform components. Documents processes, contributes solutions, participates in team discussions, and guides junior team members. Requires Kinaxis proficiency, data integration knowledge, data modeling familiarity, analytical problem-solving, and effective cross-functional communication.

The description indicates a payroll-focused analyst role within the Payroll team, but provides no specific responsibilities, qualifications, technologies, salary, travel, or clearance requirements.

Designs, develops, tests, and maintains scalable ETL pipelines using Azure services and streaming technologies. Collaborates with architects, analysts, scientists, and business stakeholders to ensure data accuracy and integrity. Builds data models and documentation, optimizes workflows, monitors production data flows, troubleshoots issues, implements data-quality controls, and integrates cloud and on-premise storage while supporting governance, security, and privacy standards.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

Build and maintain data pipelines using Databricks, Python, SQL, and Spark. Apply data modeling, cloud data services, version control, and CI/CD practices. Support streaming pipelines, workflow orchestration, data governance, security controls, and downstream BI requirements. Experience with AWS or Azure is required, while Databricks certification, streaming technologies, orchestration tools, and Power BI exposure are preferred.

Build and maintain data pipelines, metrics, KPIs, reports, and interactive dashboards for access-control and client-services environments. Analyze multi-source data to identify trends, anomalies, and security risks. Develop predictive models and AI workflows, automate reporting, improve data quality, and communicate actionable insights to stakeholders and leadership. Collaborate with operations, engineering, cybersecurity, and governance teams.

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.

Build and maintain KPIs, reports, dashboards, ETL/ELT pipelines, and data refresh processes for access-control and digital workplace services. Analyze multi-source data to identify trends, anomalies, and security risks; develop predictive models and AI workflows; and present actionable insights to leadership. Partner with operations, engineering, cybersecurity, and governance teams while improving data quality, integrity, and reporting automation.

Develop and support scalable ETL/ELT data pipelines, data warehouses, and data lake solutions. The role requires advanced SQL, Snowflake and IDMC/IICS expertise, pipeline troubleshooting, data security, performance optimization, and experience with big data technologies including Kafka, Spark, and Hadoop. Responsibilities include full lifecycle data engineering, root cause analysis, data reconciliation, cloud platform integration, and collaboration with cross-functional teams using agile and project management practices.

Develop and support complex applications using Java, Scala, Spark, Hadoop, Python, SQL, Databricks, and AWS. Responsibilities include systems analysis, application design, development, testing, implementation, production support, risk assessment, process improvement, and advising junior analysts. The role requires independent judgment, stakeholder consultation, problem solving, and delivery of scalable low-latency and batch or real-time data solutions.

Lead the architecture, development, operation, and optimization of scalable data pipelines and reporting services for Elsevier’s Search and AI Platform. Own AWS infrastructure, data quality, observability, governance, reliability, and performance. Partner with product and engineering teams on technical delivery, mentor engineers, establish software engineering standards, and contribute to broader platform architecture and roadmap decisions.

Leads the design, development, and operation of scalable data pipelines and report-generation services for Elsevier’s Search and AI Platform. Owns architecture, AWS infrastructure, data quality, observability, governance, reliability, cost optimization, and production support. Partners with product and engineering teams on technical solutions and delivery roadmaps, establishes engineering standards, and mentors engineers. The role supports large-scale content ingestion and AI-generated medical content evaluation systems.

Designs and builds end-to-end data platforms on Google Cloud for regulated-sector clients. Responsibilities include developing reliable pipelines, modeling scalable data systems, optimizing performance and cost, ensuring data governance and regional compliance, and applying cloud networking and security practices. The role requires strong SQL and Python skills, client-facing communication, and ownership of project outcomes.

Lead the architecture and engineering of scalable enterprise data platforms, including lakehouses, warehouses, streaming systems, APIs, and data products. Establish engineering standards for architecture, testing, governance, observability, security, and deployment while mentoring data engineers. Champion AI-assisted engineering, reliable data practices, modern integration patterns, and governed access. Partner with technical and business stakeholders to guide platform modernization and deliver secure, high-quality data solutions.

Design, build, operate, and optimize scalable data pipelines, integrations, APIs, data models, and cloud data solutions. Develop batch, streaming, and event-driven workflows while implementing testing, observability, governance, security, and data quality controls. Support AI-enabled applications, including RAG and semantic models, and use AI-assisted engineering practices responsibly. Troubleshoot production systems, automate deployments, contribute to architecture decisions, and collaborate with technical and business teams.

Plan, design, execute, and validate migrations from legacy mainframe systems to Google Cloud. Assess DB2, VSAM, IMS DB, and file-based data; build extraction, transformation, validation, and loading processes; automate migration utilities; and ensure data integrity, security, governance, compliance, and business continuity. Collaborate with application, infrastructure, cloud, and business teams on large-scale modernization initiatives.

Designs, builds, and maintains scalable batch and real-time data pipelines, warehouses, lakes, and models supporting analytics and machine learning. Responsibilities include data quality monitoring, database optimization, cloud infrastructure management, governance, security, CI/CD, documentation, and production troubleshooting. The role collaborates with data scientists, analysts, and product teams to deliver reliable datasets and infrastructure.

Designs, builds, and manages scalable clinical data pipelines supporting clinical trials. Responsibilities include ingesting, integrating, transforming, validating, and delivering data from EDC/eCRF, CTMS, and other source systems into data lakes and warehouses. The role requires Python, SQL, AWS, Spark, Airflow, APIs, and CI/CD expertise, along with clinical data workflow knowledge, data quality and traceability practices, and collaboration with technical and clinical stakeholders.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Designs and develops scalable data architectures, cloud data solutions, data warehouses, business analytics tools, and complex data pipelines. The role involves data wrangling, exploration, and discovery across heterogeneous sources, using AWS, SQL, Python, and relational database technologies. The engineer also contributes to data engineering innovation, knowledge sharing, and project planning while enabling data-driven organizational decision-making.

Designs and maintains scalable data pipelines and ETL/ELT workflows using Python, SQL, and Azure Databricks. Manages large structured and semi-structured datasets, data models, monitoring, quality controls, troubleshooting, and production support. Integrates LLM and Generative AI capabilities, including APIs, embeddings, prompt engineering, and RAG workflows, into data solutions. Collaborates with engineering, analytics, and business teams while following security, CI/CD, documentation, and maintainability best practices.

Design, build, and optimize GCP data ecosystems, including pipelines, data lakes, warehouses, storage architectures, and processing infrastructure. Gather requirements, model data, troubleshoot complex issues, tune performance, monitor systems, and support new functionality. Collaborate directly with clients and cross-functional teams, while mentoring junior data engineers and leading technical improvement discussions.

Build and own end-to-end Microsoft Fabric solutions across data integration, lakehouse and warehouse architectures, semantic models, Power BI reporting, AI-enabled analytics, governance, security, testing, deployment, and production support. Develop reusable pipelines, notebooks, dimensional models, and automated deployments while optimizing performance and cost. Collaborate in agile delivery, resolve cross-component issues, document solutions, and mentor junior engineers.