Top Data Engineer Jobs in Gurgaon

Design, build, and maintain ELT/data pipelines and workflow orchestration using Airflow, Python, and PySpark. Develop custom connectors, implement DataOps and CI/CD for data, monitor and validate data flows, collaborate with cross-functional teams, and document data workflows and governance.

Join the Event Streaming and Data Replication platform as a Data Engineer to design, build, and operate cloud-native data platforms. Work with Airflow, Spark, Kubernetes, and Python; deploy containerized apps on AWS/Azure; use IaC (Terraform/CFT/ARM); implement CI/CD (GitHub, Artifactory, Jenkins); configure monitoring and alerts; and collaborate across teams to deliver highly available, scalable solutions.

Design, build, and maintain scalable ELT/data pipelines and connectors using Airflow, Python, and PySpark. Implement DataOps and CI/CD for data workflows, monitor and troubleshoot pipelines, validate data quality, collaborate with cross-functional teams, and document data processes and governance.

Design, build, and maintain ELT/data pipelines and workflow orchestration using Airflow, Python, and PySpark. Develop custom connectors to ingest diverse data, implement DataOps and data CI/CD pipelines, monitor and troubleshoot pipelines, validate data quality, collaborate with cross-functional teams, document workflows, and work with large-scale distributed datasets and visualization tools.

Design, build, and optimize ETL pipelines and data warehouses using SQL, AWS Glue, and S3. Investigate and resolve data issues, develop Python-based solutions and small web apps, and own requirements, design, and end-to-end delivery for change requests.

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.

The role involves creating and managing data ingestion pipelines, optimizing data workflows, and ensuring data governance and compliance in a rapidly growing fintech environment.

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.

The Data Engineer will design and maintain data models, develop BI solutions, integrate data, and optimize data flows while collaborating with stakeholders.

Develop high-quality operational engineering capabilities and solutions. Collaborate with business partners, create designs, and optimize data processing pipelines using various technologies.

Design, build, and maintain Microsoft Fabric/OneLake lakehouse solutions across Bronze/Silver/Gold layers. Create reusable ingestion and CDC pipelines, implement transformations and data quality frameworks, optimize Spark/PySpark/Python/SQL workloads, support Power BI Direct Lake, enforce governance (Purview, RBAC, PII/PHI), and troubleshoot production issues.

Design, build, and maintain high-volume ETL/ELT pipelines across Hadoop and AWS using Spark/PySpark, Scala, Glue, EMR, Step Functions and orchestration tools. Optimize performance, enforce data governance, support incidents, collaborate with stakeholders, and mentor junior engineers.

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.

Design, deploy, and maintain AWS and Databricks data infrastructure, data lakes, and scalable ETL pipelines. Build custom ingestion services and connectors using Airbyte, Go, and Python; optimize performance, reliability, monitoring, and data quality. Implement cloud data governance, security, access controls, encryption, and compliance practices while collaborating with platform engineers, analysts, and stakeholders.

Design, build, and optimize scalable Databricks-based ETL/ELT pipelines using PySpark, Spark SQL and Delta Lake. Manage clusters, integrate cloud storage and streaming sources, ensure data quality and observability, and collaborate with data scientists and stakeholders to support analytics and ML workloads.

Lead design, development, and optimization of complex data systems and pipelines; oversee data engineering projects; ensure data quality; align solutions with business goals; mentor junior engineers and provide strategic guidance.

Lead delivery of cloud-based data engineering projects on AWS and Snowflake. Design high-level solutions, guide development and testing, optimize performance, deploy to production, manage risks, and communicate with stakeholders while mentoring team members and ensuring agile, timely delivery.

Design, build, and maintain ETL/ELT pipelines and data integration workflows into Snowflake using Azure Data Factory and DBT. Write and optimize SQL for extraction and transformation, monitor pipeline performance, enforce data quality and governance, and collaborate with analysts, architects, and DevOps in cloud-native environments.

Lead development and optimization of complex data systems and pipelines; oversee data engineering projects; ensure data quality; mentor junior engineers; align data solutions with business goals and provide strategic guidance.

Lead delivery of cloud-based big data projects: design, implement, and deploy ETL/data pipelines on AWS and Snowflake. Mentor team, collaborate with stakeholders, manage risks, and ensure high-quality, timely project delivery.

Design, build, and operate scalable batch and real-time data pipelines and analytics platforms. Partner with enterprise customers to define requirements, own technical delivery from discovery to adoption, and present architectures to stakeholders. Drive data governance, observability, CI/CD, and platform optimization while mentoring junior engineers and collaborating with Product, Engineering, and Customer Success teams to enable analytics and AI/ML applications.

Triage and resolve Master Data Management (Party, Risk, Reference) issues in Stibo STEP, run targeted data-cleansing sprints, monitor data quality dashboards, support releases/UAT, and maintain documentation and playbooks to prevent recurring data issues.

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.

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.