Job description
Designation: Data Engineer (Microsoft Fabric – ETL & Migration) Location: Aundh, Pune Experience: 3 to 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 Py. Spark / 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, One. Lake). Strong expertise in Py. Spark 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.