Top Data Engineer Jobs in Gurgaon

Builds and maintains scalable data pipelines, data architecture, ETL infrastructure, and analytics tools. The role assembles complex datasets, improves data delivery and infrastructure scalability, supports cross-functional stakeholders, and enables actionable business insights. Required expertise includes SAS, Python, Java, MATLAB, SQL and NoSQL databases, Hadoop-based data warehousing, MapReduce, Hive, and Presto. The position requires 5–8 years of related experience and a bachelor’s degree or equivalent experience.

Build and maintain scalable data ingestion pipelines, implement automated data quality checks, clean and validate datasets, analyze anomalies and trends, manage databases and backups, and coordinate with internal teams and external providers to resolve data issues. The role supports trading and research teams in a high-frequency trading environment and requires strong ownership of data reliability and availability.

Leads the design, development, and maintenance of scalable ETL/ELT pipelines and data products across distributed, multi-cloud environments. Defines data engineering architectures, resolves complex production issues, and designs efficient data solutions. Provides technical leadership and mentorship while collaborating with architects, engineers, and business stakeholders. Requires expertise in big data, AWS services, data warehousing, databases, data modeling, orchestration, streaming, security, observability, version control, containers, and modern development practices.

Develop and maintain data pipelines, models, database structures, and orchestrations on Microsoft Azure. Transform vessel, project, campaign, and third-party data into high-quality datasets and reports. Collaborate with business stakeholders to refine requirements, support data scientists and report developers, and design modern cloud data solutions. The role requires strong data modeling, ER modeling, SQL or programming experience, and at least five years of relevant experience.

Lead SAP MM/PP master data architecture, governance, integration, automation, and lifecycle management for semiconductor supply chain and production data. Manage materials, BOMs, routings, production versions, and engineering change records; integrate SAP PLM with ERP; support planning systems, custom enhancements, data migration, and ECC-to-S/4HANA transformation. Partner with business and technical teams to deliver governed, scalable solutions and improve data quality through automation and AI-enabled platforms.

Design, develop, and optimize scalable GCP data pipelines and distributed data systems using Big Data technologies. Collaborate with analytics, data science, and business teams to deliver secure, high-performance data solutions. Implement data quality, governance, security, infrastructure automation, and deployment practices. Troubleshoot pipeline issues, document architecture and workflows, and drive continuous improvement while mentoring junior team members and supporting enterprise-scale data platforms.

Develop and maintain SAP ABAP solutions, customizations, reports, workflows, and integrations. Build SAP data extraction and integration pipelines using ODP, CDS Views, OData, and SAP Datasphere. Translate business requirements, especially SAP SD processes, into scalable technical solutions and integrate SAP data with cloud platforms. Collaborate with functional consultants, data engineers, stakeholders, and external partners while independently resolving technical issues and strengthening internal SAP capabilities.

Design, build, and maintain centralized data pipelines, warehouses, lakes and hubs. Assemble complex datasets, implement ETL processes, build analytics tools and reports, support cross-functional teams, ensure secure cross-region data separation, and create product data tools to enable insights on acquisition and operational metrics.

Designs, develops, optimizes, and maintains scalable data pipelines and workflows using Azure and Databricks. Responsibilities include ETL/ELT development, data modeling, validation, monitoring, troubleshooting, performance tuning, regulatory compliance, and data quality assurance. The role collaborates with business teams, analysts, architects, and developers in an Agile environment to support enterprise reporting, analytics, and insurance-related data use cases.

Build and manage scalable ETL pipelines, data models, Airflow workflows, and dbt transformations for Zoom’s marketing analytics infrastructure. Develop complex SQL and Python solutions, implement data quality monitoring, and collaborate with analysts and marketing stakeholders. The role supports campaign performance, attribution modeling, customer journey analytics, and reliable reporting through production-grade AWS data solutions.

Designs and maintains HR data infrastructure for People Analytics. Builds ETL pipelines, data models, Airflow workflows, dbt transformations, SQL queries, and data quality monitoring systems. Partners with analysts and stakeholders to deliver reliable, scalable datasets and workforce insights. Requires strong SQL, Python, AWS, Airflow, and dbt expertise, plus experience translating business requirements into production-grade data solutions.

Build and maintain ETL/ELT pipelines, data warehouses, data lakes, dashboards, and automated reporting systems. Ensure data quality, governance, security, and reliability while optimizing data architecture and workflows. Collaborate with Operations, Supply Chain, Marketing, Finance, and leadership teams to solve business problems using data and AI-driven solutions.

Build Gather AI’s data foundation from scratch by creating PostgreSQL extraction pipelines, dbt models, semantic metrics, data quality controls, tenant isolation, lineage, and documentation. The role includes warehouse data movement, ingestion-boundary validation, CI/CD delivery, safe backfills, on-call ownership, and collaboration across product, ML, security, and integration teams. Engineers will initially support the Drone product and later expand the platform to additional robotics and warehouse products.

Build and evolve a reliable, scalable data platform supporting analytics, credit models, experimentation, and operational reporting. Responsibilities include creating batch and near-real-time ingestion frameworks, data modeling and semantic layers, governance and security controls, lineage, PII handling, observability, and platform roadmaps. The role also involves scaling backend services, partnering with Product and Data Science teams, and supporting large distributed datasets using modern orchestration, warehouse, and programming technologies.

Designs and maintains Power Automate workflows, REST API integrations, scalable data pipelines, structured evidence datasets, data-quality processes, and Power BI dashboards. The role enables self-service analytics, explores AI solutions for clinical report data, reduces manual work, and establishes reusable documentation and methods. It collaborates with stakeholders across time zones and supports regulated clinical or pharmaceutical environments.

Designs and maintains Power Automate workflows, REST API integrations, scalable data pipelines, data-quality processes, and Power BI dashboards. Migrates structured evidence data into analytics-ready datasets, enables self-service reporting, and explores AI solutions for converting unstructured clinical content into structured data. The role also documents reusable solutions and collaborates with stakeholders across time zones.

Build and maintain MDM data pipelines, Airflow DAGs, data models, transformations, and data products. Optimize SQL, BigQuery, and pipeline performance while applying data governance and secure-by-design principles. Collaborate with global technical and non-technical stakeholders, document solutions, support CI/CD, and work independently within Agile delivery frameworks. The role requires financial services experience, MDM platform expertise, strong risk management awareness, and familiarity with change, incident, and problem management.

Lead data engineering project delivery using Snowflake, SQL, AWS, Python, PySpark, and big data technologies. Responsibilities include defining technical designs, directing development and testing, tuning and deploying applications, managing project risks, communicating with stakeholders, and guiding team members. The role requires strong cloud implementation, Agile delivery, problem-solving, and leadership skills, with preferred experience in DevOps, CI/CD, cloud migrations, insurance, and Data Vault 2.0.

Build and maintain organizational data pipelines using Python, SQL, Airflow, and Snowflake. Responsibilities include data cleansing, normalization, ingestion through APIs, files, and databases, ETL/ELT development, data modeling, pipeline monitoring, and quality optimization. Collaborate with engineering, analytics, and product teams in an Agile environment, using integration tools and software development best practices.

Lead data engineering for WPP Media’s reporting platform, including deploying Adverity data collectors, developing SQL and dbt transformations on Google Cloud Platform, maintaining data quality checks, monitoring automated jobs, and troubleshooting data issues. Translate business requirements into technical solutions, support reporting and visualization, collaborate with data providers and stakeholders, assist users, and mentor junior team members.

Build, optimize, and maintain scalable Azure data pipelines and workflows using Databricks, Spark, PySpark, SQL, and Python. Develop reporting dashboards in Power BI and Tableau, implement CI/CD automation, tune performance, troubleshoot complex data issues, and uphold data quality and reliability. Collaborate with engineers, architects, and business stakeholders to deliver production-grade data solutions while supporting governance, monitoring, alerting, and continuous improvement.

Design, build, and maintain cloud-native ELT pipelines and data models for large-scale financial datasets using dbt, BigQuery, and Airflow. Ensure data quality, reconciliation, observability, and performance; support APIs, CI/CD, and disaster recovery; collaborate with product and engineering teams and apply domain knowledge in securities and market data.

Manage Microsoft Fabric governance, security, access controls, capacity, job monitoring, compliance, and workspace hygiene. Implement data policies across Fabric and Azure, automate administrative and security tasks with APIs, scripts, and notebooks, and create operational dashboards, alerts, and compliance reports. Requires enterprise-scale data governance experience and hands-on expertise with Fabric administration, OneLake, Purview, Power BI, and security controls.

Design, build, and optimize scalable batch and streaming data pipelines on GCP and AWS. Develop ETL/ELT frameworks using SQL, Python, and Apache Beam; optimize BigQuery performance and costs; implement data quality, validation, monitoring, governance, lineage, and metadata tracking; and automate deployments through CI/CD. Collaborate with analytics, product, engineering, and business teams to deliver reliable data solutions for reporting and advanced analytics.

Designs and maintains ETL/ELT pipelines and data integration workflows using Azure Data Factory, Snowflake, and DBT. Develops optimized SQL transformations, monitors pipeline performance, troubleshoots reliability issues, and enforces data quality, governance, and documentation standards. Partners with stakeholders, analysts, architects, and DevOps teams to deliver cloud-based data solutions for large-scale datasets.