Top Data Engineer Jobs in Gurgaon

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.

Develop and maintain reliable data pipelines, ELT processes, workflow orchestration, custom data connectors, and CI/CD pipelines using Airflow, Python, PySpark, Hive/Trino, SQL, and Snowflake. Collaborate with cross-functional teams, monitor and troubleshoot data workflows, validate and test data, document technical processes, and support data governance.

Leads end-to-end data products from ingestion and transformation through semantic modeling, Power BI reporting, documentation, governance, and user adoption. Builds Microsoft Fabric pipelines, Lakehouse or Warehouse architectures, dimensional models, and secure, optimized Power BI solutions. Analyzes data, supports advanced analytics and AI-assisted reporting, establishes quality and CI/CD practices, collaborates with cross-functional stakeholders, and manages data engineers through objectives, reviews, and development.

Lead data quality and governance initiatives: define frameworks, perform profiling/validation/cleansing, manage metadata/catalog/lineage, implement governance tools, monitor KPIs, coordinate remediation, and train stakeholders to ensure trusted, compliant enterprise data.

Design, build, and maintain scalable ETL/ELT and real-time data pipelines on AWS. Manage Airflow DAGs, Kafka ingestion, Redshift optimization, data quality checks, API integrations, and cost optimization while collaborating with BI for reporting needs.

Design, build, and maintain production data pipelines and integrations across applications, databases, and cloud services. Support data architecture, ingestion, embedding workflows for AI, data quality, monitoring, and platform reliability. Collaborate with Full Stack and AI teams to deploy scalable cloud-based data infrastructure and establish data engineering standards and best practices.

Support ingestion, transformation, profiling, documentation, and Microsoft Fabric operations for an actuarial discovery sandbox. Build and maintain Lakehouse tables and pipelines, run data quality checks, document datasets, assist with access controls and cloud connections, and support actuarial and product teams with ad hoc data requests and troubleshooting.

Designs and maintains SAP BW/4HANA data models and pipelines, develops end-to-end BI solutions, integrates SAP and non-SAP data, and delivers structured datasets for reporting and analytics. The role includes ETL, data transformation, performance optimization, query and load monitoring, and collaboration with business stakeholders. Candidates need strong SQL, data warehousing, SAP Datasphere, SAP BW on HANA, and SAP BDC experience. Power BI, Business Objects, and ABAP are beneficial.

Design, build, and maintain scalable ETL/data pipelines and data management blueprints. Ingest, transform, and curate large complex datasets for analytics, support cloud (AWS) implementations, enable downstream consumers, and provide technical leadership and operational support to ensure data governance, quality, and automation.

Design and develop scalable data-driven solutions and pipelines using Python and SQL. Leverage Azure data services to build secure, high-performance architectures, ensuring data integrity, governance, and overall success of data projects.

Design and build scalable data pipelines and architectures on Azure using ADF, Databricks, and Synapse. Implement Kimball/Data Vault modeling, write ETL in Python and T-SQL, ensure data governance (Azure Purview), automate tests, troubleshoot issues, document processes, and collaborate across teams.

Design and develop data-driven solutions and scalable data pipelines using Python and SQL. Leverage Azure data products to build robust, secure architectures, ensuring data integrity, performance, and governance while supporting data-centric client projects.

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.

As a hands-on data engineer, you'll build and enhance scalable data platforms, develop data pipelines, and collaborate with various teams to improve data solutions and system performance.

Lead technical direction for data platform capabilities: design and maintain data pipelines, reporting models, and data governance to support trusted metrics and AI/LLM agentic workflows. Drive architectural improvements, mentor engineers, collaborate on roadmaps, and represent technical capabilities externally.

Support business analysis by gathering and documenting requirements, analyzing business processes, performing basic data analysis, and collaborating with senior analysts to learn best practices.

Design, develop, and maintain cloud data infrastructure, data pipelines, data models, and storage solutions on Azure. Ensure data quality, integrate with cross-functional teams, optimize distributed systems, and establish data engineering best practices.

Design, build, and maintain cloud data pipelines and architectures using SQL, Python, Snowflake, and AWS. Implement CI/CD for data workflows, use developer assistance tools, and collaborate across teams to deliver reliable data 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, develop, and coordinate cloud data solutions using Snowflake, Python, and AWS. Support product releases, deployments, testing, and post-install support. Collaborate with architects and tech leads, troubleshoot issues, and maintain documentation. Contribute to agile squads, adopt new technologies, and enable Fidelity Wealth business data needs.

Design, build and maintain ETL mappings and data models; write SQL and Snowflake queries; manage ETL/infrastructure on AWS (EC2); use Informatica, Control-M and ServiceNow; script in Python/Shell/PowerShell; collaborate with onshore/offshore teams and define BI delivery strategy.

Design, build, and maintain cloud-based ETL/data pipelines in Snowflake. Extract and transform data from heterogeneous sources, optimize SQL performance, implement monitoring, and collaborate across teams. AWS and Python experience are advantageous.

Lead database design, data modeling, migrations, and schema evolution across MySQL/Postgres and GCP databases. Design and operate config-driven ETL/ELT pipelines and streaming ingestion, enable CI/CD, troubleshoot data flows, and mentor offshore DB/ETL engineers.

Design, build, and maintain cloud data pipelines and streaming solutions using Java, Kafka, AWS services, SQL/warehouse technologies, and Python. Implement CI/CD, automated testing, and resilient microservice-based systems. Collaborate in a small agile team and ensure secure identity/authentication controls.

Design, build and optimize ETL pipelines and cloud data workflows using Python, PySpark, AWS Glue and Lambda. Implement data models and schemas in PL/SQL/PostgreSQL, integrate AWS services (SNS, Step Functions), and troubleshoot pipelines for performance, scalability and reliability.