Top Data Engineer Jobs in Gurgaon

Analyze business and user requirements across financial services value streams, define technical requirements, develop cloud solution options, and manage business and IT stakeholders. The role requires understanding existing framework capabilities, complex data models, cross-functional solution design, clear documentation, team coordination across regions, and familiarity with the SDLC from design through testing and implementation.

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, 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 and support batch and streaming data ingestion pipelines, real-time monitoring and alerting systems, data warehouses, data lakes, marts, aggregations, metrics, and KPIs. Partner with product teams to define complex data sets, maintain data dictionaries and catalogs, document data assets, and generate actionable insights into product efficacy, marketing, customer behavior, and retention.

Build, maintain, test, and optimize scalable data architectures and platforms. Extract, transform, and load data for analysts and scientists; source and integrate new data; apply data quality testing, cleansing, monitoring, and modelling. Collaborate with technology and architecture teams, adopt DevOps practices, resolve root causes, manage platform costs, and develop knowledge of data structures and metrics.

Lead the design and implementation of scalable data pipelines, warehouses, data models, and automation across the organization. Establish engineering best practices, data governance, security, quality, and observability. Partner with data scientists, engineers, product teams, and stakeholders to deliver reliable data solutions. Evaluate new tools, improve processes, mentor team members, and contribute as both a technical architect and hands-on coder.

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.

Build and support data integration and real-time streaming platforms. Responsibilities include data modeling, transformation, cloud-based data engineering, CI/CD and DevOps practices, Kafka and CDC solutions, topic and schema management, connector and stream-processing pipelines, legacy-to-modern platform integration, data mapping, quality management, and stakeholder collaboration.

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.

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 requirements.

Design, develop, and maintain scalable Microsoft Fabric and Azure data solutions. Build Lakehouse, Data Warehouse, OneLake, ETL/ELT pipelines, data transformations, and integrations using Python, PySpark, SQL, and Fabric tools. Implement medallion architecture, optimize data models and workloads, establish data quality monitoring, and support CI/CD deployments. Collaborate with analytics and business teams to deliver enterprise-scale data platforms.

Build and maintain scalable data pipelines, migrate legacy architectures to cloud-based AI-ready platforms, develop semantic layers, and support analytics, business intelligence, and AI/ML use cases. The role collaborates with stakeholders, data scientists, analysts, and product teams while ensuring data quality, security, governance, documentation, and workflow optimization.

The job description does not provide responsibilities or qualifications. The title indicates a data engineering role focused on Microsoft Azure and Microsoft Fabric.

Lead data quality and governance initiatives: define frameworks, perform profiling/validation/cleansing, manage metadata/catalog/lineage, implement governance tools, monitor KPIs, coordinate remediation, and train stakeholders to ensure trusted, compliant enterprise data.

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.

Build and maintain batch and streaming data pipelines across AWS, Azure, and GCP using Databricks, Spark, PySpark, SQL, and Delta Lake. Implement medallion architecture, CDC, SCD, data quality, governance, security, metadata, and lineage controls. Configure storage, orchestration, CI/CD, monitoring, troubleshooting, and performance optimization. Collaborate with architects, data scientists, engineers, analysts, and product teams while documenting pipelines, transformations, testing, and operational procedures.

Design, build, and maintain scalable data pipelines and ETL/ELT workflows using Azure Data Factory, Databricks, PySpark, Python, and SQL. Transform large datasets, optimize data ingestion and queries, monitor pipeline performance, resolve data quality issues, support data modeling, document engineering standards, and contribute to cloud data infrastructure improvements. The role also involves collaboration with architects and senior engineers and requires familiarity with Azure Synapse, Delta Lake, governance, and security.

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.

Build and own reliable data ingestion and transformation pipelines across AWS, S3-Iceberg, Redshift, Glue, Fivetran, Airflow, and dbt. Design medallion architecture, migrate legacy jobs, implement monitoring and data quality controls, optimize infrastructure costs, and partner with analysts and analytics engineers to deliver governed data for reporting and decision-making.

Principal Data Engineer responsible for developing and supporting Databricks pipelines, integrating heterogeneous healthcare data sources, modeling unified data products, and ensuring data quality and availability. The role partners with business stakeholders, product teams, data engineers, and clinical leaders to gather requirements, create datasets, support ad hoc analytics, enable machine learning and AI use cases, and assist with acquisition integrations. Extensive healthcare data, cloud, SQL, PySpark, and Python experience is required.

Leads data engineering strategy across multiple projects and teams, drives innovation in engineering practices and model optimization, represents the team in executive meetings, and develops long-term data engineering and infrastructure plans.

Build and maintain scalable data products using Snowflake, dbt, and Airflow. Responsibilities include dimensional data modeling, SQL transformations, pipeline orchestration, data quality testing, exploratory analysis, SLA monitoring, documentation, and stakeholder requirement gathering. Partner with analytics, product, engineering, and business teams to deliver reliable, production-ready datasets while following software engineering, CI/CD, testing, and deployment practices.

Design, build, and optimize end-to-end data solutions using Microsoft Fabric, Azure Synapse, Databricks, Azure Data Factory, and Power BI. Develop scalable real-time and batch pipelines, data models, semantic and dimensional models, dashboards, reports, and self-service analytics. Apply medallion architecture, DataOps, monitoring, security, governance, quality controls, and performance tuning. Integrate Azure Machine Learning and streaming technologies when needed while delivering enterprise data and BI solutions for clients.