Top Data Engineer Jobs in Gurgaon

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, develop, and maintain scalable data pipelines and ETL processes using PySpark. Ensure data quality and governance, optimize pipeline performance, automate data operations, lead team decisions, collaborate across teams, and mentor junior engineers to support reliable data migration and processing across systems.

Design, develop, and maintain Snowflake-based data solutions and ETL pipelines. Ensure data quality, optimize pipeline performance, document workflows, and collaborate with teams while mentoring juniors.

Design, build, and maintain ETL/ELT pipelines and data integration workflows into Snowflake using Azure Data Factory and DBT. Write optimized SQL, monitor and troubleshoot pipelines, enforce data quality and governance, and collaborate with analysts, architects, and DevOps in a cloud-native environment.

Design, develop and optimize ETL pipelines and SQL code using SSIS, T-SQL, Teradata and Denodo. Perform data profiling, source-target mapping, SQL tuning, testing and implementation. Support rollouts, fixes and enhancements, and improve data-loading programs and services. Communicate effectively with stakeholders and end users.

Design, develop, and maintain data solutions and scalable ETL pipelines using Databricks. Ensure data quality, governance, and reliable data migration across systems. Lead and mentor a team, make technical decisions, optimize workflows, and troubleshoot data issues to support cross-team data accessibility.

Design, build, and maintain cloud-based data solutions and ETL pipelines. Ensure data quality, optimize workflows for performance and cost, troubleshoot pipeline issues, collaborate with stakeholders, and mentor junior team members while providing cross-team technical leadership and advisory support.

Design, develop, and maintain scalable data pipelines and ETL processes using Google BigQuery, Python, and Data Build Tool. Ensure data quality, model schemas, optimize queries, troubleshoot integrations, lead and mentor team members, and coordinate cross-functional data engineering efforts.

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.

No responsibilities or qualifications were provided in the job posting. The listing only contains the job title: Data Engineer.

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.

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 and build scalable ELT pipelines and data models using Databricks, dbt, Airflow and BI tools. Lead data governance, observability, and self-serve analytics initiatives; partner with data scientists, product and business stakeholders; mentor engineers and drive enterprise-wide data platform and architecture decisions to enable reliable, governed, and AI-enabled data products.

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.

Lead development of automated data management, ETL/ELT pipelines and reporting. Build Python-based API integrations with advertising platforms, maintain data warehouses (Redshift/BigQuery), implement data quality checks, monitor jobs, troubleshoot issues, and deliver interactive dashboards. Mentor team members and manage third-party data provider relationships.

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.

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.

The Senior Azure Data Engineer will design, develop, and maintain data infrastructure and pipelines, ensuring data quality, integrity, and alignment with business objectives. Responsibilities include collaborating with teams and establishing best practices for data engineering.

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

Design, build, and maintain scalable AWS-based ETL/ELT data pipelines and data platform solutions. Develop SQL and Python code, create tests and documentation, monitor and optimize pipeline performance, and collaborate with BI, AI/ML, and infrastructure teams while following best practices and data governance.

Build and enhance enterprise data pipelines into Databricks Unity Catalog and Delta Lake. Improve remediation and entity-matching layers (including D&B DataBlocks), integrate APIs, and support LLM-assisted remediation and CMG redesign projects.