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.