Top Data Engineer Jobs in Gurgaon

Design and build data services and pipelines supporting machine learning products. Develop automation tools for model deployment, maintain production data systems, improve infrastructure, participate in code reviews, and collaborate across engineering, data science, and product teams. The role requires expertise in distributed systems, large-scale data processing, CI/CD, container orchestration, and AI-enabled workflow improvements.

Design, develop and optimize ETL pipelines and SQL code using SSIS, T-SQL, Teradata and Denodo. Perform data profiling, source-target mapping, SQL tuning, testing and implementation. Support rollouts, fixes and enhancements, and improve data-loading programs and services. Communicate effectively with stakeholders and end users.

Build, maintain, test, and optimize scalable data architectures and platforms. Extract, transform, cleanse, model, and load data for analysts and data scientists. Source and integrate new data, apply DevOps practices, troubleshoot issues, manage platform costs, and collaborate with technology, architecture, and business stakeholders to deliver secure, cost-effective data solutions.

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.

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.

Build and optimize scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Develop agentic workflows for data validation, troubleshooting, and automation. Create reusable data engineering frameworks, implement quality checks and monitoring, optimize workloads, support deployment and productionization, and resolve complex production issues with cross-functional teams.

Design and implement data pipelines, optimize data processing and storage, ensure data accuracy and performance, provide technical support, and collaborate with data scientists, analysts, and stakeholders to resolve data issues and meet data requirements.

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 maintain ETL pipelines supporting customer experience survey data, integrating sources into Redshift and downstream platforms. Ensure data quality, lineage, monitoring, scalable processing, and reliable delivery for CX reporting and automation. Collaborate with analysts, product owners, and engineers on data modeling, Agile ceremonies, code reviews, documentation, and platform improvements.

Own the architecture, technical direction, reliability, cost efficiency, and adoption of Atlassian’s analytical data platform. Build foundational ingestion, transformation, orchestration, compute, and consumption components; establish multi-tenant standards and SLOs; and lead cross-team technical initiatives. The role requires hands-on development, distributed systems expertise, batch and streaming architecture design, cloud experience, and mentorship of senior engineers.

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 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.

Design, build, and maintain scalable Azure-based data pipelines and systems (Databricks, Data Lake, Synapse, ADF). Collaborate with cross-functional teams, ensure data quality and integrity, optimize performance, monitor and troubleshoot systems, and stay current with industry trends to improve solutions.

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.

Designs and optimizes scalable data architectures, ingestion pipelines, ELT/ETL transformations, cloud data warehouses, and orchestration workflows. The role uses Python, SQL, dbt, Spark, and platforms such as Snowflake, Databricks, and BigQuery. Responsibilities include data quality validation, query and cost optimization, dimensional modeling, access controls, data masking, compliance, and enterprise data warehouse migrations. Familiarity with streaming technologies and mandatory certification in Snowflake, Databricks, or Google Cloud is required.

Build scalable data pipelines and ETL/ELT processes using modern data platforms. Perform exploratory analysis, validate data, develop models supporting analytics and real-time insights, and collaborate with data science, product, engineering, and customer success teams. Write tested Python code, contribute to data quality initiatives, and deliver end-to-end features in an agile environment.

Build scalable data pipelines and ETL/ELT processes using Python and modern data platforms. Perform exploratory analysis, validate data, develop data models, and support analytics and real-time insights. Collaborate with data scientists, analysts, engineers, and product teams while delivering tested, reliable features. The role also contributes to data quality policies, processes, scorecards, and product operations reporting.

Designs and optimizes enterprise data pipelines using Azure Data Factory, Azure Data Lake Storage, SQL, Python, and cloud data platforms. Owns pipeline reliability, troubleshooting, automation, CI/CD, data lineage, documentation, code standards, and technical audits. Supports analytics and media teams while leading modernization initiatives involving Databricks, dbt, and DevOps. Serves as a subject matter expert for pipeline architecture, orchestration, schema evolution, monitoring, governance, and high-availability data processing.

Build scalable data engineering solutions on GCP, including BigQuery warehouses, Dataproc Spark/Hadoop clusters, Airflow orchestration, Dataflow pipelines, and Pub/Sub integrations. Develop automated ETL/ELT pipelines with Python, SQL, Scala, or Cloud Data Fusion; design OLAP and OLTP data models; and use GCP infrastructure, CI/CD, monitoring, and logging tools. Collaborate with analysts and business stakeholders while solving complex data challenges and adapting to evolving cloud technologies.

Designs and implements data pipelines and processing workflows, optimizes data storage and processing performance, ensures data accuracy, provides technical support, and collaborates with data scientists, analysts, and stakeholders to resolve data issues and meet data requirements.

Design, build, and maintain ETL/ELT pipelines using Azure Data Factory, Snowflake, and DBT. Develop optimized SQL for extraction and transformation, integrate data from multiple sources, ensure data quality and governance, monitor and troubleshoot pipelines, and collaborate with analysts, architects, and DevOps in a cloud-native environment.

Designs and develops scalable Microsoft Fabric and Azure data platforms, including ETL/ELT pipelines, APIs, integrations, data warehouses, lakehouses, and analytical data models. The role supports Power BI reporting, Salesforce integration, data quality, monitoring, CI/CD, performance optimization, production troubleshooting, and collaboration with business, CRM, reporting, and engineering teams.

Design and deliver cloud-native data platforms, scalable big data pipelines, lakehouses, warehouses, transformation frameworks, BI solutions, and ML-enabled data workflows. Lead client-facing pre-sales engagements, technical workshops, proof-of-concepts, executive conversations, and enterprise data modernization initiatives. Build reusable architectural IP, influence senior stakeholders, and translate ambiguous business challenges into production-ready solutions using AWS and modern data technologies.