Top Data Engineer Jobs in Gurgaon

Design, build, test, deploy, and maintain enterprise data platform components. Automate data pipelines, support platform users, optimize performance and scalability, and provide L2/L3 operational support using Airflow, Python, Snowflake, dbt, and Azure/ADLS. Collaborate with product managers and stakeholders through the SDLC and document outcomes.

Design, build, and maintain cloud-native data pipelines and ETL/ELT workflows for high-volume trade lifecycle data. Integrate DTCC sources (RTTM, Settlement Web) into a Unified Trade Record, process ISO 15022 messages, manage trade exception databases, optimize SQL Server performance, and ensure data quality, lineage, governance, and observability while collaborating with business stakeholders.

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.

As a Data Engineer, your role involves designing, developing, and maintaining data solutions, creating data pipelines, ensuring data quality, and implementing ETL processes with a focus on machine learning and collaboration with teams.

Design, build, and maintain Business Translation Layer (BTL) data pipelines using SQL and DBT on Azure Databricks. Implement CI/CD with GitHub and Azure DevOps/Harness, monitor and optimize pipeline performance, diagnose data processing issues, ensure data quality and secure coding practices, and collaborate with teams for system integration.

Design, develop, and maintain scalable data pipelines and ETL processes using BigQuery and Python. Ensure data quality, optimize query performance, manage large datasets, and implement cloud data warehousing and data lake solutions. Lead and mentor team members, coordinate cross-functional troubleshooting, and enforce data management best practices.

Design, build, and maintain scalable cloud-native data pipelines and dimensional data models using DBT, SQL, and AWS services. Implement infrastructure-as-code with AWS CDK, automate CI/CD, ensure data quality and governance, optimize performance, and collaborate with cross-functional teams and offshore partners to deliver analytics-ready data assets.

Support client transformation engagements by analyzing business requirements, assisting development of transformation solutions, documenting solution designs, performing basic consulting tasks, and collaborating with senior consultants to learn methodologies and build skills.

Design, implement, and maintain data pipelines using Elasticsearch and data warehousing solutions, integrate data sources, and ensure data accuracy while collaborating with cross-functional teams.

Design and build data systems and pipelines, develop data processing workflows, optimize data storage and performance, ensure data accuracy, provide technical support, and collaborate with data scientists and analysts to resolve data issues.

As a Data Engineer, you'll optimize data pipelines and models using PySpark, SQL, and GCP. Responsibilities include processing large datasets and designing enterprise-scale data platforms.

Design, develop, optimize, and maintain scalable data pipelines and workflows using Azure and Databricks. Implement data validation, monitoring, and performance tuning. Collaborate with stakeholders to ensure high-quality, governed data for analytics and insurance use cases while working in an Agile delivery model.

The role involves designing and building data systems using various technologies, especially within Microsoft Azure, and requires experience in system integration and data warehousing.

Design, build, and maintain scalable cloud-native data pipelines for trade lifecycle processing. Ingest, transform, and normalize high-volume financial data (DTCC, ISO15022), implement MQ/API-based ingestion, optimize SQL Server performance, maintain UTR schemas and trade exception databases, ensure data quality, lineage and governance, and collaborate with reconciliation, settlements, and operations stakeholders.

Design and build a greenfield, multi-layer data warehouse and semantic metrics layer; separate analytics from OLTP; ensure data quality, traceability, tenant isolation, and high SLAs; enable linking of structured records to drone imagery/video; deliver self-service data access, observability, and infrastructure automation to scale across products and tenants.

Deliver and maintain a global big-data processing platform, support cloud migration, write tested high-quality code, build and maintain automation test suites in Azure DevOps, perform manual/load/exploratory testing, collaborate with senior developers, estimate sprint tasks, and mentor junior engineers.

Design, build, and maintain scalable cloud-based ETL/ELT data pipelines (Python, PySpark, SQL, Snowflake) supporting P&C insurance pricing, rating, underwriting, and actuarial analytics. Collaborate with actuaries, data scientists, analysts, and cloud engineers to deliver reusable data frameworks, batch/incremental ingestion, data quality, and transformations for GLM and predictive models.

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.

Design, build, and maintain scalable batch and real-time data pipelines and ingestion frameworks. Develop modular, tested ETL/ELT transformations with dbt, ensure data quality, modeling, governance, lineage, and security. Monitor, troubleshoot, and optimize pipelines while collaborating with analytics, data science, and product teams to deliver production-ready data for reporting and advanced analytics.

Design, build, and maintain scalable batch and real-time data pipelines and ingestion frameworks. Develop modular ELT/ETL transformations with dbt, ensure data quality, model data for analytics, monitor pipelines, troubleshoot performance issues, and implement governance, lineage, and documentation to enable analytics and data science teams.

We are seeking Data Engineers to migrate dashboards from Hadoop to GCP, ensuring data consistency, accuracy, and collaborating with cross-functional 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.

Design and implement data models and ETL solutions. Reverse-engineer physical data models, analyze integration challenges, support data architecture and movement strategy, and ensure long-term, business-aligned data solutions.

As a Data Engineer, you will design, build, and maintain scalable data platforms and pipelines, optimizing workflows and ensuring security.