Data Engineer (Microsoft Fabric)

Job Description – Data Engineer (Microsoft Fabric – ETL & Migration) Location- Pune Aundh Experience- 3–6 years of experience in data engineering, with strong exposure to cloud data platforms and migration projects (AWS to Azure/Fabric preferred). Qualification- B.E./B.Tech/MCA/Relevant Degree in Computer Science, IT, Data Engineering, or related field Contract – 6 Month On Contract Role Overview We are looking for a Fabric Data Engineer to support enterprise data platform modernization by migrating legacy SQL-based systems to Microsoft Fabric. This role focuses on ETL/ELT pipeline development, converting SQL stored procedures into Fabric Spark notebooks, and delivering production-grade data layers for analytics and reporting. Key Responsibilities · Convert SQL stored procedures and legacy transformation logic into PySpark / Spark SQL notebooks in Microsoft Fabric. · Design and build end-to-end ETL/ELT pipelines using Fabric Data Factory, Dataflows Gen2, and Fabric pipelines. · Develop Gold-layer data marts optimized for Power BI dashboards and enterprise analytics. · Implement data ingestion and transformation pipelines across Bronze, Silver, and Gold layers. · Ensure data quality through validation, reconciliation, and comparison with legacy systems. · Optimize data processing performance, query execution, and pipeline efficiency. · Monitor and troubleshoot ETL pipelines, notebook runs, and data refresh failures. · Implement CI/CD pipelines using Azure DevOps and Git for deployment across Dev, UAT, and Production environments. · Collaborate with BI developers, business stakeholders, and ERP teams to translate business requirements into scalable data solutions. Required Skills & Qualifications · Strong experience in ETL/ELT pipeline development and data engineering. · Hands-on experience with Microsoft Fabric (Data Factory, Dataflows Gen2, Notebooks, OneLake). · Strong expertise in PySpark and Spark SQL. · Advanced SQL skills including stored procedures and query optimization. · Strong understanding of data modeling (star schema, dimensional modeling). · Experience with Azure DevOps, Git, and CI/CD pipelines. · Understanding of medallion architecture (Bronze, Silver, Gold layers). Key Success Metrics · Successful conversion of SQL workloads into Fabric notebooks. · Stable and scalable ETL pipelines with minimal failures. · High-quality, business-ready datasets for reporting. · Improved pipeline performance and reduced processing time. · Reliable production data platform supporting enterprise analytics.