Top Data Engineer Jobs in Chennai

Provides technical leadership for Azure data engineering projects, including solution architecture, data modeling, pipelines, data integration, code quality, QA, performance optimization, governance, security, and documentation. Collaborates with stakeholders and project managers to define scope, manage risks, and deliver scalable, efficient solutions. Requires strong Python, SQL, PySpark, Databricks, Azure Data Factory, Delta Lake, Synapse, Spark, data warehousing, governance, and Azure DevOps expertise.

Design, build, and optimize scalable AWS-based data platforms and ETL/ELT pipelines using Python, SQL, PySpark, Spark, Glue, and Airflow. Responsibilities include data transformation, workflow orchestration, quality monitoring, performance tuning, production troubleshooting, architecture discussions, code reviews, and establishing engineering standards. The role independently owns projects, resolves complex issues, mentors engineers, and contributes to technical decisions.

Develop AI-powered product features for local discovery, including personalized recommendations, content curation, search, and ranking. Build data pipelines by scraping, cleaning, and structuring datasets for machine learning models. Collaborate with product, design, and engineering teams to integrate AI into the platform, while continuously improving machine learning capabilities and user experiences.

Leads the design, development, and maintenance of scalable ETL/ELT pipelines and data products across distributed, multi-cloud environments. Defines data engineering architectures, resolves complex production issues, and designs efficient data solutions. Provides technical leadership and mentorship while collaborating with architects, engineers, and business stakeholders. Requires expertise in big data, AWS services, data warehousing, databases, data modeling, orchestration, streaming, security, observability, version control, containers, and modern development practices.

Develop and maintain data pipelines, models, database structures, and orchestrations on Microsoft Azure. Transform vessel, project, campaign, and third-party data into high-quality datasets and reports. Collaborate with business stakeholders to refine requirements, support data scientists and report developers, and design modern cloud data solutions. The role requires strong data modeling, ER modeling, SQL or programming experience, and at least five years of relevant experience.

Designs, builds, and maintains scalable data pipelines and architectures supporting Denials AI workflows. Responsibilities include developing ETL/ELT pipelines, modeling PostgreSQL data, optimizing SQL, monitoring pipeline performance, troubleshooting incidents, enforcing data quality and HIPAA compliance, automating deployments, documenting workflows, conducting code reviews, and mentoring junior engineers.

Build and maintain scalable ETL/ELT pipelines and data architectures for Denials AI workflows. Develop PostgreSQL data models, optimize SQL, monitor pipeline health, troubleshoot incidents, enforce data quality and HIPAA compliance, and automate deployments using AWS services and infrastructure-as-code. The role also includes code reviews, mentoring junior engineers, documentation, cross-functional requirements gathering, and on-call incident response.

Develop and maintain Oracle APEX applications, Oracle Analytics Server/OBIEE reporting solutions, BI Publisher reports, and Oracle database objects. Build dashboards, workflows, integrations, REST APIs, and data models; write complex SQL and PL/SQL; troubleshoot and tune application and database performance; support deployments, testing, production issues, documentation, and stakeholder collaboration.

Develop and maintain SAP ABAP solutions, customizations, reports, workflows, and integrations. Build SAP data extraction and integration pipelines using ODP, CDS Views, OData, and SAP Datasphere. Translate business requirements, especially SAP SD processes, into scalable technical solutions and integrate SAP data with cloud platforms. Collaborate with functional consultants, data engineers, stakeholders, and external partners while independently resolving technical issues and strengthening internal SAP capabilities.

Designs and maintains scalable data ingestion, transformation, integration, and ETL/ELT pipelines. Diagnoses data quality issues, performs root-cause analysis, implements sustainable fixes, and optimizes SQL and data-processing workloads. Integrates internal and external systems through APIs and batch interfaces, supports CI/CD and DevOps practices, and ensures data accuracy, governance, security, and compliance. Collaborates with architects, analysts, QA teams, and stakeholders to deliver reliable data for analytics, reporting, and downstream applications.

Develop and support Laserfiche document-digitization and workflow solutions, including custom C#/.NET and VB/VB.NET components, SQL Server integrations, OCR, metadata processing, automated ingestion, and repository services. Troubleshoot production workflows and Windows services, implement monitoring, testing, deployment controls, and documentation, and integrate Microsoft 365 and Graph APIs. Collaborate with infrastructure, DevOps, security, QA, and application teams while providing technical leadership, code reviews, mentoring, and knowledge transfer.

Designs and maintains Salesforce Data Cloud solutions for audience segmentation, activation, real-time ingestion, and data governance. Responsibilities include developing SQL queries, configuring Data Extensions, Data Model Objects, Data Streams, Calculated Insights, and Computed Traits; architecting cloud integrations with Snowflake, AWS, Azure, or GCP; resolving data quality issues; and implementing consent and governance frameworks.

Leads enterprise architecture strategy for GMPD Technology, aligning business objectives with scalable business, application, data, and technology architectures. Develops roadmaps, standards, governance, and reference architectures; guides solution delivery and evaluates risk, ROI, vendors, and emerging technologies. Partners with technical and business stakeholders, supports cloud and data architecture initiatives, communicates with senior leadership, and promotes architecture practices across complex transformation programs.

Develop and optimize Supabase’s Data API based on PostgREST. Responsibilities include improving the PostgreSQL driver and performance, developing features, fixing bugs, resolving incidents and escalated support issues, improving observability, and debugging integrations across the Supabase platform. The role requires strong Haskell development, PostgreSQL and SQL expertise, Linux debugging, open-source contribution, and technical user support.

Build and evolve a reliable, scalable data platform supporting analytics, credit models, experimentation, and operational reporting. Responsibilities include creating batch and near-real-time ingestion frameworks, data modeling and semantic layers, governance and security controls, lineage, PII handling, observability, and platform roadmaps. The role also involves scaling backend services, partnering with Product and Data Science teams, and supporting large distributed datasets using modern orchestration, warehouse, and programming technologies.

Designs, builds, and configures scalable data solutions using Databricks. Responsibilities include developing robust data pipelines, optimizing data workflows, supporting large-scale data processing, integrating data, managing data quality, troubleshooting technical issues, and delivering enterprise-grade analytics solutions. The role also requires collaboration with teams and stakeholders while serving at a team lead or consultant level.

Design and evolve distributed data architectures, infrastructure platforms, and database systems at massive scale. Lead initiatives involving sharding, replication, migrations, reliability, performance, scalability, and infrastructure cost efficiency. Influence database technology choices and long-term technical strategy across engineering teams. Build reusable infrastructure capabilities, solve systemic technical problems, and lead through technical influence without formal authority.

Build scalable backend services, REST APIs, data-processing and ETL/ELT workflows using C#, .NET, Python, SQL, and cloud technologies. Integrate internal and external systems, optimize relational databases, implement security and asynchronous processing, and support testing, CI/CD, deployments, troubleshooting, and production readiness. Preferred work includes messaging, serverless cloud services, document processing, OCR, AI/LLM integrations, and enterprise workflow applications.

Own Lyric’s end-to-end data platform, including semantic layers, entity models, ontologies, knowledge graphs, ingestion, data contracts, schema evolution, orchestration, storage, query systems, and reliability for a multi-tenant product. Set technical architecture, write production code, lead design reviews, create RFCs, handle incidents, mentor senior engineers, and guide systems toward production scale. The role requires building data platforms from zero, strong systems reasoning, and hands-on technical leadership.

Develop statistical and machine learning models to optimize Akamai’s global mapping service. Analyze massive telemetry streams, build predictive observability tools and automated data pipelines, define reliability metrics, investigate outages, and guide architecture and technology investment decisions. Collaborate with product engineers and communicate complex quantitative insights to technical and executive stakeholders.

Build scalable data pipelines and ETL/ELT processes using Python and modern data platforms. Perform exploratory analysis, validate data, develop data models, and support analytics and real-time insights. Collaborate with data scientists, analysts, engineers, and product teams while delivering tested, reliable features. The role also contributes to data quality policies, processes, scorecards, and product operations reporting.

Build scalable data pipelines and ETL/ELT processes using modern data platforms. Perform exploratory analysis, validate data, develop models supporting analytics and real-time insights, and collaborate with data science, product, engineering, and customer success teams. Write tested Python code, contribute to data quality initiatives, and deliver end-to-end features in an agile environment.

Designs, builds, optimizes, and monitors scalable ETL/ELT pipelines, data models, warehouses, and lakes. Develops Power BI datasets, DAX measures, semantic layers, refresh pipelines, and performance optimizations. Ensures data quality, governance, security, and row-level security while supporting CI/CD releases and production troubleshooting. Partners with BI and business stakeholders, mentors junior engineers, and establishes data engineering best practices.

Extract and maintain support data, build pipelines and dashboards, analyze ticket trends and customer journeys, identify automation opportunities, and translate insights into prioritized improvements. The role implements Zendesk workflows and APIs, supports AI and automation initiatives, runs experiments, measures business impact, and communicates findings to technical and non-technical stakeholders.

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.