Top Data Engineer Jobs in Gurgaon

Design and implement data solutions by translating business problems into technical designs; lead modules, mentor junior team members, validate architectures, develop ETL/warehouse/reporting solutions, and support project delivery including planning, testing, deployment and client collaboration.

As a Data Engineer II, you will manage complex problem resolutions, develop automation solutions, and optimize business processes while collaborating on project plans and improving customer-focused services.

The Data Engineer will design, develop, and maintain ETL/ELT data pipelines, leveraging PySpark and Databricks to process large datasets, ensuring data quality, reliability, and performance optimization.

Build and operate a scalable data platform: design and maintain ETL/ELT pipelines, Bronze/Silver/Gold data layers, data models and semantic layers, manage migrations and schema changes, ensure data quality/observability, enable analytics and future event-driven/recommendation systems, and use AI-assisted workflows to accelerate engineering.

Lead design and implementation of scalable, secure data platform solutions. Build data pipelines, lakes, and warehouses using Snowflake, Trino, PostgreSQL, SQL Server, Apache NiFi, Python, SQL and AWS. Drive CI/CD and code review practices, collaborate with stakeholders, and apply asset management domain knowledge to enable business-critical analytics and data use cases.

Design, develop, and maintain data solutions and ETL pipelines. Ensure data quality, perform transformations and cleansing, troubleshoot data issues, lead scalable data architecture design, mentor junior engineers, and coordinate with stakeholders to translate requirements into technical solutions.

As a Data Engineer, you will leverage data to drive business outcomes, develop data integration frameworks, and support data quality and lifecycle management.

Build and manage the data layer for equities trading, develop scalable data pipelines, ensure high-quality research datasets, and set data standards.

The AI Data Engineer will manage the full lifecycle of data solutions, including ETL pipelines, analytics, and AI-driven workflows, while collaborating with various stakeholders to support strategic and operational needs.

The Data Engineer builds and maintains data lakes and warehousing solutions, writes ETL transformations, and manages infrastructure as code. They troubleshoot performance issues, collaborate with teams, and participate in cross-functional initiatives.

Design, build, and maintain large-scale data pipelines and ETL/ELT workflows using Spark and Scala. Implement data models and warehousing on Snowflake and cloud (AWS/GCP). Ensure data quality, governance, security, and performance optimization while collaborating with data scientists, analysts, DevOps, and stakeholders.

Design, build, and maintain scalable data pipelines on GCP using BigQuery, Dataflow, Cloud Functions and SQL/Python. Optimize ETL/ELT workflows, ensure data quality and performance, monitor pipelines, and collaborate with analysts and stakeholders. Support hybrid GCP/Azure architectures when needed.

Design, build, and maintain ETL data pipelines using Talend and Snowflake. Extract data from APIs, reverse-engineer Alteryx workflows, apply Data Vault modeling, implement CI/CD and AWS data services, document designs, and collaborate with architects, SMEs, and developers to deliver robust cloud-based data warehousing solutions.

Design, develop, and optimize AWS Glue ETL pipelines integrating data from S3, RDS, Redshift, and other AWS services. Transform data into analytics-ready formats, ensure data quality and security, tune performance, orchestrate workflows (Step Functions/Airflow), monitor jobs, and collaborate with analysts and stakeholders to deliver scalable data lake and warehouse solutions.

Design, build, and maintain scalable ETL/ELT pipelines and data warehouses using Snowflake, AWS Glue, Python, and SQL. Optimize performance, implement CI/CD with Azure DevOps, ensure data quality and governance, collaborate with analytics teams, and troubleshoot pipeline issues.

Design, build, and operate large-scale batch and real-time data platforms and pipelines. Take end-to-end ownership—from architecture to deployment—debug and optimize distributed open-source systems, collaborate across teams, and drive data engineering best practices.

Lead the Data Engineering team in designing and building ETL pipelines for the Internal Data Platform, ensuring scalability and performance, while collaborating with security and governance teams.

Design, build, and maintain scalable GCP-based data pipelines and analytics infrastructure using BigQuery, Dataflow, Spark, Hive, Python, and SQL. Support cloud migration, schema design, data validation, pipeline monitoring, performance optimization, automation, and documentation to enable enterprise analytics, governance, and high-availability data workflows.

Lead design and build of scalable data pipelines, ETL, and streaming ingestion. Embed modern data techniques, automate pipelines, manage data modelling/warehousing, ensure data quality and regulatory compliance, and engage stakeholders to deliver cost-effective data platform solutions.

Design and build scalable data pipelines, ETL, streaming ingestion and transformations. Collaborate with stakeholders to model data, automate quality testing, reduce platform costs, and embed new data techniques across the organisation while ensuring security and operational controls.

Data Engineer responsibilities include designing and building scalable data management systems, optimizing data pipelines, managing data warehouses, and ensuring data quality and compliance.

Build and maintain scalable ETL pipelines on GCP, transform large/disconnected datasets, automate and optimize data delivery, support stakeholders, and apply data engineering best practices including testing and release management.

Design, build, and maintain scalable ETL/ELT pipelines and microservices for marketing and product data, ingesting from ad platforms (especially Meta Ads) and event systems. Enable campaign performance tracking, lead funnel analysis, attribution, and revenue reporting. Ensure data quality, observability, governance, and documentation while collaborating with marketing, growth, and analytics to deliver actionable datasets and drive business outcomes.

Design, build, and maintain scalable ETL/ELT data pipelines on GCP/BigQuery using Python and Spark. Integrate systems (APIs, Oracle EBS), implement workflow orchestration, ensure data quality and security, monitor performance, and document processes while collaborating with analysts and business teams.

Data Engineer responsible for building and maintaining ETL pipelines, data processing workflows, and data platforms. Must have strong hands-on experience with Healthcare Revenue Cycle Management and understanding of healthcare data workflows. Role requires hybrid (onsite + offshore) work and immediate availability.