Top Data Engineer Jobs in Gurgaon

Build and maintain data transformation pipelines, automated QA processes, and AI-assisted tools for debugging, documentation, and transformation. Develop prototype datasets, codify transformation logic, and support adoption of new technologies through mentoring, workshops, and training. Collaborate cross-functionally in an agile environment while applying Python, cloud services, SQL, CI/CD, machine learning, and data visualization tools.

Design, build, and maintain scalable data pipelines; migrate legacy architectures to cloud AI‑ready platforms; implement semantic layers; ensure data quality, security, and governance; collaborate with stakeholders and document architectures for analytics and BI.

Lead test strategy and automation for an Azure data platform modernization: validate end-to-end pipelines, ETL/ELT transformations (Medallion), data integrity in Azure SQL and Data Lake, test Snowflake/DBT integrations, develop automated SQL/Python/PySpark tests, perform performance/load testing, and track defects in Jira.

Design, build, and maintain Microsoft Fabric/OneLake Lakehouse solutions (Bronze/Silver/Gold). Create scalable ingestion pipelines (CDC, ETL/ELT), implement data quality, validation, reconciliation, and monitoring. Develop Spark/PySpark, Python, and SQL workloads, support Power BI Direct Lake, enforce governance (Purview, RBAC, PII/PHI), troubleshoot production issues, and collaborate with architects and stakeholders.

Design, build, and maintain enterprise ETL/ELT pipelines in Microsoft Fabric; ingest and transform data from diverse sources into dimensional star schemas; optimize performance and cost; implement data quality checks and monitoring; document architecture and pipelines; collaborate with analysts, stakeholders, and global engineering teams to deliver Power BI-ready analytics datasets.

Design and implement data models, perform reverse-engineering of physical models, analyze integration challenges, build and maintain ETL pipelines, and contribute to data architecture decisions to ensure scalable, long-term data solutions.

Design and implement data models, reverse-engineer physical data models from databases/SQL, resolve system integration challenges, support data architecture decisions including data movement and technology strategy.

Design and implement long-term data models, reverse-engineer physical data models from databases and SQL, solve data integration challenges, support data architecture and data movement strategy, and build/operate ETL pipelines using Snowflake, Python, AWS services and CI/CD.

Manage operational delivery and client relationships, plan and monitor project delivery, implement MIS for status and risk tracking, drive risk and governance, and lead continuous improvement and stakeholder communication.

Design and build data systems and pipelines; develop and optimize data processing workflows and storage; ensure data accuracy and performance; provide technical support and collaborate with data scientists, analysts, and stakeholders to resolve data issues.

Design and implement long-term data models, reverse-engineer physical data models from databases and SQL, analyze system integration challenges, support data architecture direction including data movement and technology strategy to ensure business value.

Design, build, and maintain data pipelines and processing workflows. Optimize data storage, ensure data accuracy and performance, provide technical support, and collaborate with data scientists and stakeholders to resolve data issues.

Design, build, and optimize data pipelines and storage solutions. Ensure data accuracy and performance, provide technical support, and collaborate with data scientists and stakeholders to resolve data issues.

Design and translate business needs into scalable data models, reverse-engineer physical models, analyse integration challenges, support data architecture and data movement strategy.

Design, build, and optimize data pipelines and storage solutions. Ensure data accuracy and performance, provide technical support, and collaborate with data scientists and stakeholders to resolve data issues.

Design, build, and optimize data pipelines and storage on Azure. Ensure data accuracy and performance, implement ETL orchestration with ADF and Databricks, support stakeholders, and resolve data issues while enforcing security and monitoring.

Design and implement data models; reverse-engineer physical schemas; analyze integration challenges; support data architecture and data-movement strategy; develop ETL pipelines and ensure data solutions meet business needs.

Design, develop, and maintain ETL pipelines, data models, and data assets. Collaborate with stakeholders for requirements, lead data validation/UAT/regression testing, create integration documentation, and implement automated data quality and validation processes.

Build and maintain cloud-based ETL/ELT pipelines and Snowflake data platforms. Implement data ingestion, transformations (SQL/DBT), data quality checks, and support analytics/reporting. Collaborate with architects, engineers, and analysts, participate in Agile processes, and assist with performance tuning, monitoring, and documentation.

As a Senior Data Engineer, you will develop and maintain data pipelines for healthcare data, ensuring data quality, reliability, and adherence to enterprise standards while collaborating with architects and stakeholders.

The Senior Data Engineer will develop data ingestion, transformation, and enrichment pipelines, ensuring data quality and reliability. Responsibilities include creating Bronze and Silver data layers, using SQL and dbt for implementation, and collaborating with architects and stakeholders to align data solutions with enterprise standards.

Build, optimize, and maintain scalable AWS-based data pipelines and data lakes (Snowflake/Databricks). Implement streaming ingestion (Kinesis/Kafka), CDC, automated data quality checks, and ML-ready feature stores. Collaborate with Product, ML, and Analytics teams, own pipeline QA, observability, and participate in on-call rotations to resolve production data issues.

Build, monitor, optimize, and maintain ETL/ELT pipelines using Google Cloud technologies. Develop complex SQL transformations, design dimensional data models, improve query performance and cloud cost efficiency, and ensure data integrity. Collaborate with analysts and stakeholders to translate SaaS and insurance metrics into technical solutions. Participate in code reviews, documentation, troubleshooting, and data warehouse lifecycle management while supporting US business hours.

Design, build, and optimize scalable GCP data pipelines and BigQuery data warehouses. Develop ETL/ELT workflows, implement data modeling, ensure data quality, optimize query performance and costs, maintain orchestration with Cloud Composer/Dataflow/Pub‑Sub, and automate deployments with CI/CD while enforcing security and governance.

Develop and maintain Microsoft Fabric and OneLake Lakehouse solutions across Bronze, Silver, and Gold layers. Build ingestion pipelines, CDC frameworks, data-quality controls, transformations, curated datasets, and secure views using Spark, PySpark, Python, and SQL. Support Power BI semantic models and Direct Lake consumption. Implement governance, lineage, security, monitoring, reconciliation, and operational controls while troubleshooting production issues and collaborating with architects, engineers, BI teams, and stakeholders.