Top Data Engineer Jobs in Chennai

Design and build data services and pipelines supporting machine learning products. Develop automation tools for model deployment, maintain production data systems, improve infrastructure, participate in code reviews, and collaborate across engineering, data science, and product teams. The role requires expertise in distributed systems, large-scale data processing, CI/CD, container orchestration, and AI-enabled workflow improvements.

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.

Design, develop, and maintain scalable data solutions, pipelines, and ETL processes using Snowflake. Ensure data quality, reliability, and performance across structured and unstructured data environments. Collaborate with cross-functional teams, troubleshoot pipelines, document workflows, contribute to technical solutions, and support junior team members.

Build, maintain, test, and optimize scalable data architectures and platforms. Extract, transform, and load data for analysts and scientists; source and integrate new data; apply data quality testing, cleansing, monitoring, and modelling. Collaborate with technology and architecture teams, adopt DevOps practices, resolve root causes, manage platform costs, and develop knowledge of data structures and metrics.

Designs and optimizes scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Builds agentic workflows for data validation, troubleshooting, and automation; develops reusable frameworks, quality controls, monitoring, exception handling, and production processes. The role also requires performance optimization, testing, deployment, production support, and collaboration with business, data engineering, and platform teams.

Designs and maintains scalable ETL pipelines, data models, databases, and cloud-based analytics workflows. Manages PostgreSQL, MySQL, MongoDB, and Google Cloud SQL environments, performs database optimization, develops AutoML solutions, and deploys data engineering pipelines to production. Works within Agile teams using Scrum or Kanban, participating in sprint planning, backlog grooming, and daily stand-ups.

Designs, develops, and maintains data pipelines and ETL processes for data generation, collection, processing, migration, and deployment. Ensures data quality, governance, storage, and retrieval performance while monitoring and optimizing workflows. Collaborates with cross-functional teams to deliver scalable data solutions, documents architecture changes, and supports junior team members. The role requires independent execution, subject-matter expertise, and at least three years of data analytics experience.

Support the assessment, validation, governance, and monitoring of AI/ML and Generative AI solutions used in cybersecurity. Evaluate model changes and outputs, design guardrails and thresholds, document validation results, and ensure alignment with regulatory, risk, responsible AI, and enterprise security standards. Collaborate with technology, risk, business, and vendor stakeholders to improve model effectiveness, explainability, and operational resilience.

Own the architecture, technical direction, reliability, cost efficiency, and adoption of Atlassian’s analytical data platform. Build foundational ingestion, transformation, orchestration, compute, and consumption components; establish multi-tenant standards and SLOs; and lead cross-team technical initiatives. The role requires hands-on development, distributed systems expertise, batch and streaming architecture design, cloud experience, and mentorship of senior engineers.

Build Gather AI’s data foundation from scratch by creating PostgreSQL extraction pipelines, dbt models, semantic metrics, data quality controls, tenant isolation, lineage, and documentation. The role includes warehouse data movement, ingestion-boundary validation, CI/CD delivery, safe backfills, on-call ownership, and collaboration across product, ML, security, and integration teams. Engineers will initially support the Drone product and later expand the platform to additional robotics and warehouse products.

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.

Leads cybersecurity data-source onboarding and integration for Zscaler’s security platform. Builds and automates Python/API-based data transformations, maps and normalizes security data, manages pipeline lifecycle activities, troubleshoots data quality, and identifies security gaps. Partners with cybersecurity SMEs and cross-functional teams to create implementation plans, communicate technical findings, and provide product improvement feedback. The role requires expertise in security platforms, diverse security-tool integrations, data modeling, SQL, and Python.

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.

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.

Supports enterprise data management and transformation initiatives by designing and executing data migration strategies. Develops automated ETL workflows using Azure Data Factory, Synapse Pipelines, or similar tools; monitors and troubleshoots migration jobs; and performs data profiling, cleansing, validation, and reconciliation. The role requires strong SQL, cloud, data architecture, governance, data quality, MDM, engineering data, and BOM experience.

Designs and optimizes scalable data architectures, ingestion pipelines, ELT/ETL transformations, cloud data warehouses, and orchestration workflows. The role uses Python, SQL, dbt, Spark, and platforms such as Snowflake, Databricks, and BigQuery. Responsibilities include data quality validation, query and cost optimization, dimensional modeling, access controls, data masking, compliance, and enterprise data warehouse migrations. Familiarity with streaming technologies and mandatory certification in Snowflake, Databricks, or Google Cloud is required.

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.

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.

Designs and develops scalable Microsoft Fabric and Azure data platforms, including ETL/ELT pipelines, APIs, integrations, data warehouses, lakehouses, and analytical data models. The role supports Power BI reporting, Salesforce integration, data quality, monitoring, CI/CD, performance optimization, production troubleshooting, and collaboration with business, CRM, reporting, and engineering teams.

Design and deliver cloud-native data platforms, scalable big data pipelines, lakehouses, warehouses, transformation frameworks, BI solutions, and ML-enabled data workflows. Lead client-facing pre-sales engagements, technical workshops, proof-of-concepts, executive conversations, and enterprise data modernization initiatives. Build reusable architectural IP, influence senior stakeholders, and translate ambiguous business challenges into production-ready solutions using AWS and modern data technologies.

Design and maintain batch and real-time data pipelines, data lakes, warehouses, marts, and reusable dbt models using Spark, Kafka, Maxwell, S3, Trino, BigQuery, and related technologies. Optimize distributed processing, data models, queries, storage, reliability, and costs. Establish data quality, observability, governance, automation, and CI/CD practices while partnering with Product, Engineering, Analytics, Data Science, and business stakeholders.

Build and operate scalable financial market data pipelines for Binance stock products and AI applications. Responsibilities include sourcing, ingesting, modeling, processing, storing, and serving real-time and historical data; designing multi-market data frameworks; optimizing Flink pipelines; and establishing quality, reconciliation, monitoring, lineage, and recovery systems. The role also evaluates vendors and data sources, defines data contracts, and collaborates with trading, product, AI, legal, and compliance teams.

Execute data migration by extracting, transforming, loading, and validating data in target systems. Build and optimize migration scripts, pipelines, and workflows while ensuring data quality, consistency, performance, and reliability. Collaborate with multiple teams and stakeholders, oversee migration strategies, make team decisions, resolve complex issues, and mentor junior engineers. The role requires expertise in data engineering, Python, AWS, ETL techniques, cloud data solutions, scripting, and automation.