Top Data Engineer Jobs in Gurgaon

Data Engineer responsibilities include designing and building scalable data management systems, optimizing data pipelines, managing data warehouses, and ensuring data quality and compliance.

Design and maintain scalable Snowflake data warehouses and ETL/ELT pipelines. Lead Oracle-to-Snowflake migrations, including assessment, mapping, transformation, validation, and reconciliation. Develop data models and advanced SQL workflows, optimize Snowflake performance, troubleshoot pipeline and data quality issues, and support secure, governed, monitored cloud data platforms. Collaborate with architects, analysts, application teams, and business stakeholders on enterprise data modernization.

Design, implement, and optimize graph data solutions for a supplier chain project. Responsibilities include graph modeling with LPG and RDF, Neo4j database development and query optimization, GCP data processing using BigQuery, GCS, and Dataproc, and integrating graph data with visualization tools. The role also involves schema and data design, SQL-based relational database work, performance tuning, and potentially ETL/ELT, streaming, API, and distributed systems development.

Build and operate a production data pipeline ingesting Indian MCA registration data. Responsibilities include raw-first S3 storage, resilient scraping and parsing, snapshot and diff processing, granular change events, scheduled batch jobs, alert digests, monitoring, anomaly detection, reference-table maintenance, and historical backfills. The role requires strong Python, PostgreSQL, testing, operational reliability, and production experience with uncooperative data sources.

The Azure Data Engineer will build data pipelines and dataflows, implement security modules, and work with Azure services while supporting business requirements and data analysis.

We are looking for a Data Engineer with skills in Python, SQL, and Azure technologies to develop Big Data applications. Responsibilities include working with Spark and Docker to handle large scale data processing and analytics.

The Data Engineer role involves analyzing raw data, building data systems, conducting complex analyses, and enhancing data quality by collaborating with data scientists and architects.

This role involves collaborating with users to build data solutions, managing AWS data technologies, and automating workflows for efficiency.

The Azure Data Engineer will implement big data pipelines using Azure technologies, collaborate with stakeholders, and utilize tools like Data Factory and Databricks.

As a Senior Data Engineer, you will develop and maintain data pipelines for healthcare data, ensuring data quality, reliability, and adherence to enterprise standards while collaborating with architects and stakeholders.

The Senior Data Engineer will develop data ingestion, transformation, and enrichment pipelines, ensuring data quality and reliability. Responsibilities include creating Bronze and Silver data layers, using SQL and dbt for implementation, and collaborating with architects and stakeholders to align data solutions with enterprise standards.

Develop Azure data pipelines and Databricks Lakehouse solutions using Azure Data Factory, Azure SQL, SQL, and PySpark. Convert Informatica PowerCenter and Oracle PL/SQL code, curate and transform structured and unstructured data, implement data models and Delta Lake tables, and support SQL Server cloud migration. Use Azure services including ADLS and ADX, automate delivery through DevOps and CI/CD, estimate project effort, and lead complex development in an agile environment.

Build and maintain cloud-based ETL/ELT pipelines and Snowflake data platforms. Implement data ingestion, transformations (SQL/DBT), data quality checks, and support analytics/reporting. Collaborate with architects, engineers, and analysts, participate in Agile processes, and assist with performance tuning, monitoring, and documentation.

Build, optimize, and maintain scalable AWS-based data pipelines and data lakes (Snowflake/Databricks). Implement streaming ingestion (Kinesis/Kafka), CDC, automated data quality checks, and ML-ready feature stores. Collaborate with Product, ML, and Analytics teams, own pipeline QA, observability, and participate in on-call rotations to resolve production data issues.

Senior Data Engineer role supporting WPP Media's AI-driven media platform: design, build and maintain scalable data pipelines and platforms, ensure data quality, and collaborate with data scientists, engineers, and media teams to integrate data with WPP Open.

Design, build, and optimize cloud data pipelines and storage solutions on AWS. Ensure performance, metadata management, and provide technical support while collaborating with stakeholders.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

Design, build, and optimize scalable GCP data pipelines and BigQuery data warehouses. Develop ETL/ELT workflows, implement data modeling, ensure data quality, optimize query performance and costs, maintain orchestration with Cloud Composer/Dataflow/Pub‑Sub, and automate deployments with CI/CD while enforcing security and governance.

As a Data Engineer, you will design, develop, and maintain data integration solutions, optimize ETL/ELT pipelines, and support data workflows for analytics and reporting.

Design, build, and maintain large-scale data pipelines and ETL/ELT workflows using Spark and Scala. Implement data models and warehousing on Snowflake and cloud (AWS/GCP). Ensure data quality, governance, security, and performance optimization while collaborating with data scientists, analysts, DevOps, and stakeholders.

Design, build, and maintain scalable ETL/ELT pipelines and data warehouses using Snowflake, AWS Glue, Python, and SQL. Optimize performance, implement CI/CD with Azure DevOps, ensure data quality and governance, collaborate with analytics teams, and troubleshoot pipeline issues.

Design, build, and maintain scalable data pipelines on GCP using BigQuery, Dataflow, Cloud Functions and SQL/Python. Optimize ETL/ELT workflows, ensure data quality and performance, monitor pipelines, and collaborate with analysts and stakeholders. Support hybrid GCP/Azure architectures when needed.

Design, build, and maintain ETL data pipelines using Talend and Snowflake. Extract data from APIs, reverse-engineer Alteryx workflows, apply Data Vault modeling, implement CI/CD and AWS data services, document designs, and collaborate with architects, SMEs, and developers to deliver robust cloud-based data warehousing solutions.

Design, develop, and optimize AWS Glue ETL pipelines integrating data from S3, RDS, Redshift, and other AWS services. Transform data into analytics-ready formats, ensure data quality and security, tune performance, orchestrate workflows (Step Functions/Airflow), monitor jobs, and collaborate with analysts and stakeholders to deliver scalable data lake and warehouse solutions.

Design, build, and operate large-scale batch and real-time data platforms and pipelines. Take end-to-end ownership—from architecture to deployment—debug and optimize distributed open-source systems, collaborate across teams, and drive data engineering best practices.