Top Data Engineer Jobs in Gurgaon

Design and implement data engineering solutions on cloud platforms; translate business problems into technical designs; lead modules, mentor junior team members, validate architectures, manage deliverables, and support project delivery and client collaboration.

As a Data Engineer, you will build data pipelines, design data models, and deploy Data Ops, collaborating with multiple teams to enhance product offerings.

Work with client and internal teams to design and implement data engineering solutions. Lead modules, translate business problems into technical designs, mentor junior staff, validate architectures, apply best practices, and support project delivery including planning, testing, deployment, and operations.

Design, develop and maintain data analytics infrastructure, build and automate ETL pipelines and curated data products, create dashboards, collaborate with SMEs and stakeholders, follow Agile workflows, and support Snowflake/Azure integration and commercialization efforts.

Design, develop, and maintain high-performance ETL solutions using SSIS and T-SQL; integrate diverse data sources; use C# and Regex for transformations; monitor QA/UAT/Production ETL jobs; implement data quality and DevOps pipelines; collaborate with stakeholders and improve ETL best practices.

Design, build, and operate scalable ETL/ELT pipelines using PySpark and AWS data services. Orchestrate workflows with Apache Airflow, implement AWS Glue jobs and Data Catalog, manage Lake Formation permissions, publish datasets for BI, and deliver QuickSight visualizations while ensuring data quality and performance.

No role-specific responsibilities provided in the posting. Typically, a Unit Manager - Data Governance leads the data governance function: defining and enforcing data policies, managing data quality and metadata, coordinating stakeholders, ensuring regulatory compliance, and supervising a team to implement governance processes.

As a Data Engineer, you'll design and maintain ETL pipelines, integrate data, ensure data quality, and collaborate in an Agile environment.

The Lead Azure Data Engineer will design, develop, and optimize enterprise data ecosystems on Microsoft Cloud, focusing on building scalable data pipelines and real-time processing solutions while leading a team and collaborating with other functions.

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.

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.

As a Data Engineer, you will design and develop data pipelines, ensuring data quality and scalability. You'll collaborate with teams to meet business needs and mentor junior members.

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

The role involves data modeling, troubleshooting complex issues, optimizing storage architectures and mentoring junior engineers while working with GCP and Python.

The Snowflake Data Engineer will develop and manage large-scale data pipelines, collaborate with stakeholders, and optimize data engineering solutions using Snowflake.

Design, build, and optimize scalable data pipelines on Azure using Databricks and Spark. Implement CI/CD, performance tuning, debugging, and collaborate with stakeholders to deliver production-grade data solutions.

The Azure Data Engineer role requires developing data engineering solutions using Azure technologies. Responsibilities include implementing data pipelines, performing data analysis, and collaborating with stakeholders on data-driven projects.

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

The Data Engineer will build and maintain data pipelines, ensure data quality, and work with various teams in an Agile environment.

Maintain and monitor web scraping configurations, ingest and transform scraped data into the data lake, detect and resolve pipeline issues, produce stakeholder reports, and build scalable ETL pipelines and data architectures to support analytics.

The Data Engineer II will manage and support cloud-based data platforms, ensuring stable operations and collaborating with teams for data ingestion and analytics.

The Data and Application Integration Engineer will design and implement application integrations, migrate legacy processes, manage data quality, and support CI/CD pipelines.

Design, implement, and operate production Kafka-based real-time pipelines and CDC (Debezium/Postgres). Build streaming apps (Kafka Streams/Flink), deploy on Kubernetes across AWS/Azure, use IaC (Terraform/Helm), implement monitoring/alerting, and enable downstream ingestion for analytics and lake houses.

The Data Engineer designs, develops, and maintains data pipelines and infrastructure, ensuring data accuracy and accessibility while collaborating with data teams.

Graduate-level internship to gain hands-on experience building large-scale data solutions using Databricks, SQL, PySpark and Azure. Collaborate with global teams, learn Azure DevOps, integrate AI/ML into data pipelines, and progressively take on independent engineering tasks.