Top Data Engineer Jobs in Gurgaon

Design and implement data federation and lakehouse architectures (Starburst/Trino), build scalable ETL/ELT pipelines using Python and Spark, optimize Spark and federated queries, manage Delta/Iceberg/Hudi tables, and enforce governance, access control, and data masking for analytics and AI use.

Design, deploy, and monitor data collection pipelines (Adverity). Build ETL/DBT transformations in Google Cloud Platform, implement data quality checks, troubleshoot automated jobs, support dashboards (Google Data Studio/PowerBI), translate business needs to technical specs, mentor junior staff, and manage third-party data provider relationships.

Build, operate, and scale ingestion pipelines for marketing data (ad platforms, ecommerce, analytics). Normalize and transform data into shared schemas, ensure multi-tenant reliability, observability, and security, and partner with AI/data-science teams to expose clean data for agents.

Support design, development, and implementation of application and A2A integrations; migrate legacy processes to a centralized platform; build data integration pipelines and workflows using tools like ADF, Boomi, Snaplogic, or Workato; assist with data mapping, profiling, validation, automation, CI/CD, and adherence to data governance and security standards.

Design, build and maintain high-volume ETL/ELT pipelines across Hadoop and AWS. Develop distributed PySpark/Scala data processing, implement serverless data architectures (Glue, Lambda, Step Functions), optimize workflows (partitioning, file formats), ensure data governance/security, orchestrate jobs (Airflow/Control-M), troubleshoot Spark performance, and collaborate with stakeholders to deliver scalable data warehousing solutions.

Lead design and implement Microsoft Fabric-based data architecture and Lakehouse patterns. Own ingestion, pipeline design, bronze/silver/gold layers, data quality, profiling, validation, and analytics-ready feature datasets. Partner with actuarial teams, provide technical direction and code reviews, coordinate Fabric configuration and security, and document data models, transformation logic, and operational procedures.

Designs and operates scalable data and machine learning platforms using Databricks, Python, SQL, Spark, and PySpark. Builds batch, streaming, and production ML pipelines; manages Delta Lake, Unity Catalog, Workflows, MLflow, governance, data quality, observability, and model deployment. Partners with data scientists and business teams, optimizes performance and costs, contributes to platform architecture, mentors engineers, and documents operational practices.

The Senior Data Engineer will design, build, and maintain data platforms and pipelines, ensuring data security and efficiency while collaborating with stakeholders.

Design, build and maintain cloud-oriented big data solutions for ingestion, processing, cleaning and exposition. Improve data quality, implement ETL and data-warehouse architectures, and collaborate with DDS/Data Foundation teams to ensure platform reliability.

Design, implement, and optimize Apache DolphinScheduler workflows and ETL pipelines. Integrate with Hadoop, Spark, Flink, Hive, and Kafka. Develop custom plugins, troubleshoot performance and scalability issues, monitor job execution and resource allocation, and maintain CI/CD, version control, and infrastructure automation while collaborating with data engineers and DevOps teams.

Design, build, and optimize PySpark applications and ETL pipelines to process large-scale datasets from relational, NoSQL, file, and streaming sources. Ensure data quality, implement error handling, and collaborate with analysts, scientists, and architects to deliver performant data solutions.

Design, develop, test, and deploy high-performance Spark/Scala data processing applications and ETL pipelines on Cloudera (CDH). Optimize Spark and Cloudera platform performance, integrate HDFS/Hive/Impala/HBase/Kafka, ensure data integrity/security, and implement version control and CI/CD for production pipelines.

Lead the design, build, and ownership of a greenfield data platform and scalable batch and real-time pipelines. Embed AI across the data lifecycle, ensure data quality and governance, optimize pipeline performance, document best practices, and lead and mentor a team of data engineers while collaborating with cross-functional stakeholders.

As a Senior Engineer for Strategic Data Solutions, you will manage data warehousing, reporting, and analytics for internal operations and external partners. Responsibilities include collaborating with business units, providing insights, and ensuring high-quality data reporting.

The Data and Application Integration Engineer will design and implement application integrations, migrate legacy processes, manage data quality, and support CI/CD pipelines.

Design and own end-to-end data platform architecture, build ingestion and transformation pipelines, model multi-tenant datasets, optimize Postgres and ClickHouse performance/cost, implement orchestration and observability, define data quality and reconciliation standards, and mentor data/product engineers.

As a Data Engineer, you will design and develop data pipelines, ensuring data quality and scalability. You'll collaborate with teams to meet business needs and mentor junior members.

Own and automate end-to-end incentive calculation pipelines, translate business rules into auditable data workflows, build monitoring and reconciliation dashboards, ensure accuracy and auditability across multiple products and countries, and partner with Sales, Finance, and field teams to resolve discrepancies and support escalations.

Build and maintain end-to-end data pipelines using PySpark and SQL on Databricks/Delta. Establish data modeling and pipeline best practices, create AI-ready analytical datasets, troubleshoot pipeline issues, and collaborate with stakeholders to embed business logic and optimize reliability, efficiency, and performance.

As a Data Engineer, you'll build and monitor large-scale data pipelines, develop the DBT setup for data transformation and solve customer problems using your SQL skills.

Design, build, and optimize high-volume data pipelines using Python, Databricks and Spark on Azure. Create and maintain automation test suites (Azure DevOps), perform manual/load/exploratory testing, collaborate with BAs and developers to meet sprint goals, and assist with sprint estimation and delivery.

Design, build, and operate scalable data engineering services and ingestion pipelines (CDC/streaming). Develop and optimize a centralized analytics warehouse, data models, and aggregation strategies. Build APIs, enforce multi-tenant security and data governance, monitor pipeline health, improve automation and observability, and ensure production reliability through strong engineering practices.

The Senior Data Scientist & Engineer will build and manage data infrastructure, analyze product usage, build models, track metrics, run experiments, and communicate insights to guide product decisions.