Top Data Engineer Jobs in Gurgaon

Design, build, and maintain Azure-based data infrastructure and pipelines. Implement data models and schemas, ensure data quality, optimize distributed storage and processing (Databricks/PySpark), and establish engineering best practices while partnering with cross-functional teams.

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.

Design, develop, and maintain data solutions and ETL pipelines to generate, collect, process, and migrate data. Ensure data quality, implement instrumentation and guardrails for agentic GenAI systems, and build testing/evaluation harnesses for agent performance and regression testing.

Build and operate data-driven processes and insights for a quick-commerce startup. Own business objectives end-to-end, analyze customer and business data, surface KPIs and actionable insights, coordinate cross-functional delivery, and design scalable operational solutions in a fast-changing 0-to-1 environment.

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 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, 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, data scientists, and analysts; document architectures and best practices to support analytics, BI, and AI/ML initiatives.

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 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, 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.

Lead QA for Data/BI solutions: define QA processes and metrics, validate data accuracy in SQL/SSIS, create and execute test cases (manual and automated), coordinate QA for releases, run ETL/BI tests, diagnose and track defects, and maintain quality monitoring dashboards.

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, build, and maintain scalable data pipelines and semantic data models in dbt and Snowflake. Profile and ingest client data, implement data quality testing and version control, and enable self-service analytics via Tableau. Collaborate with stakeholders to deliver reporting and analytic solutions, and support ML/NLP initiatives.

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.

Design, build, and maintain ETL/ELT pipelines to Snowflake using Azure Data Factory and DBT. Develop optimized SQL transformations, ensure data quality and governance, troubleshoot pipeline performance, and translate insurance domain requirements (policy, claims, underwriting, billing, actuarial) into technical solutions while collaborating with analysts, architects, and DevOps in a cloud-native environment.

Leads architecture and development of large-scale batch and real-time data platforms and pipelines. Ensures data quality, governance, reliability, observability, scalability, and cost efficiency while building self-service capabilities. Supports analytics, machine learning, and generative AI teams with trusted data infrastructure. Establishes engineering standards, mentors engineers, and drives platform health across multiple teams. The role requires expertise in data integration, distributed processing, data modeling, cloud platforms, CI/CD, and infrastructure automation.

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.