Top Data Engineer Jobs in Gurgaon

Lead end-to-end customer data integration and migration projects using SQL, ETL, and scripting. Act as technical advisor, design tailored solutions, manage 10–15 projects, document requirements, mentor juniors, and collaborate with government stakeholders and cross-functional teams to improve and automate data workflows.

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.

The Big Data Engineer will support existing applications and work on new design and implementation requirements, utilizing big data technologies and tools.

The Data Analyst prepares, analyzes, and validates data, applying statistical methods and visualization techniques to derive insights and support business objectives. Communicates findings effectively and collaborates with Data Engineers for model and dashboard implementation.

The role involves data analytics with a focus on querying large data sets, visualization using Tableau, and applying quantitative skills, predominantly in the FMCG and Food & Distribution sectors.

The Senior Data Engineer - II will design data pipelines, optimize ETL workflows, manage databases, and support Power BI reporting for business decisions.

Design and implement data federation and lakehouse architectures (Starburst/Trino), build scalable ETL/ELT pipelines using Python and Spark, optimize Spark and federated queries, manage Delta/Iceberg/Hudi tables, and enforce governance, access control, and data masking for analytics and AI use.

The Data Visualization Developer will create and support dashboards in Power BI, develop SQL scripts, perform regression testing, and troubleshoot existing scripts.

Lead the modeling of the Customer Data Platform, defining event taxonomy and ensuring reliable datasets for analytics, product insights, and machine learning. Collaborate with various teams to maintain data consistency and shape the architecture of the behavioral data platform.

The Senior Data Engineer will design and maintain pipelines for event data ingestion and validation, ensuring operational reliability and consistency for analytics.

The Senior Data Engineer will build and maintain data lakes, data warehousing solutions, and oversee the data pipeline lifecycle in a B2B multi-tenant SaaS solution, including coding, monitoring performance, and collaborating with teams.

Design, build, and optimize scalable ETL/ELT pipelines and big-data processing on Databricks using PySpark. Implement orchestrated workflows (Airflow/Dagster/Prefect), ensure data quality/security, enable analytics and ML, and contribute to CI/CD and DevOps practices for data platforms on AWS (Redshift, Glue, S3, Lambda).

Build, operate, and scale ingestion pipelines for marketing data (ad platforms, ecommerce, analytics). Normalize and transform data into shared schemas, ensure multi-tenant reliability, observability, and security, and partner with AI/data-science teams to expose clean data for agents.

Build and maintain large-scale PySpark/Spark data reconciliation pipelines on Hadoop/Big Data platforms; integrate with Kafka, APIs and microservices; support capital markets regulatory reporting and reconciliations; lead globally distributed teams, manage stakeholders, and deliver solutions with strong application design and optional UI integration.

Design, build, optimize, and deploy large-scale Spark/Scala data processing and ETL pipelines on Cloudera (CDH). Collaborate with data teams, ensure data integrity and security, troubleshoot performance, and implement version control and CI/CD for Spark applications.

Design, build, and optimize end-to-end data pipelines for large structured and unstructured data. Implement near-real-time ETL, data validation, monitoring, and performance optimization. Collaborate with stakeholders, document designs and workflows, and provide technical guidance to the team.

Design, implement, and optimize Apache DolphinScheduler workflows and ETL pipelines. Integrate with Hadoop, Spark, Flink, Hive, and Kafka. Develop custom plugins, troubleshoot performance and scalability issues, monitor job execution and resource allocation, and maintain CI/CD, version control, and infrastructure automation while collaborating with data engineers and DevOps teams.

Design, build, and optimize PySpark applications and ETL pipelines to process large-scale datasets from relational, NoSQL, file, and streaming sources. Ensure data quality, implement error handling, and collaborate with analysts, scientists, and architects to deliver performant data solutions.

Design, develop, test, and deploy high-performance Spark/Scala data processing applications and ETL pipelines on Cloudera (CDH). Optimize Spark and Cloudera platform performance, integrate HDFS/Hive/Impala/HBase/Kafka, ensure data integrity/security, and implement version control and CI/CD for production pipelines.

Design, model, and optimize relational and analytical data structures for OLTP and data warehouse systems. Build ER diagrams, define modeling standards, manage metadata with tools like Erwin/ER Studio, and drive data cataloging/lineage using Alation/Collibra. Perform performance tuning, sandbox POCs, handle time-series/history, and collaborate with engineers, architects, and product teams while documenting and enforcing governance.

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.

Build and operate automated data quality checks, monitoring, alerting, and dashboards; integrate quality into data pipelines and governance tooling; automate anomaly detection and support auditors, analysts, and stewards to surface issues in near-real-time.

The Senior Data Engineer will build and optimize data pipelines in Azure Databricks, migrate existing pipelines from Azure Synapse, and design scalable data models to support a financial services client's data platform modernization.

As a Senior Engineer for Strategic Data Solutions, you will manage data warehousing, reporting, and analytics for internal operations and external partners. Responsibilities include collaborating with business units, providing insights, and ensuring high-quality data reporting.

The Snowflake Senior Data Engineer will lead projects in data engineering, utilizing Snowflake and various AWS services to optimize data workflows and storage, while also guiding the team and collaborating with stakeholders.