27 Sep
|
Ignitho
|
Chennai
Senior MS Fabric Data Engineer
Position Title: Senior MS Fabric Data Engineer
Experience Required: 7 - 10+ years overall, with 2+ years hands-on in Microsoft Fabric or equivalent experience in Azure Synapse/Azure Databricks with strong Fabric readiness.
Role Summary
We are seeking a Senior Data Engineer with strong Microsoft Fabric expertise to design, build, and optimize scalable data pipelines and Lakehouse solutions on Azure. This role involves hands-on engineering across the Microsoft Fabric platform, including OneLake, Data Factory, Lakehouse, Warehouse, Notebooks, Spark, and Power BI Direct Lake, while collaborating with architects, analysts, and business stakeholders.
Key Responsibilities
- Design, build, and maintain end-to-end data pipelines in Microsoft Fabric using Data Factory pipelines/dataflows, Notebooks, and Spark jobs.
- Build and manage Lakehouse and Warehouse architectures on OneLake, implementing Bronze/Silver/Gold layers.
- Develop and optimize PySpark/Spark SQL transformations within Fabric Notebooks.
- Implement Delta Lake tables with proper partitioning, compaction, and schema evolution handling.
- Integrate real-time data using Fabric Real-Time Intelligence, Eventstream, and KQL Database.
- Build semantic models and enable Direct Lake mode in Power BI.
- Migrate existing workloads from Synapse Analytics / Azure Databricks / SSIS to Microsoft Fabric where applicable.
- Implement data quality, validation, and error-handling frameworks.
- Apply data governance and security controls using Fabric workspace roles, OneLake security, and Microsoft Purview integration.
- Optimize Fabric capacity utilization and cost.
- Write clean, modular, reusable code and maintain version control using Git integration with Fabric workspaces.
- Collaborate with data architects on solution design and participate in code reviews.
- Mentor junior engineers.
- Troubleshoot pipeline failures, performance bottlenecks, and data discrepancies in production.
- Document data flows,
transformation logic, and pipeline dependencies.
Required Skills & Experience
- 7+ years in data engineering with strong hands-on experience in Microsoft Fabric, or equivalent depth in Synapse/Databricks with demonstrated Fabric upskilling.
- Strong working knowledge of
- Fabric Lakehouse & Warehouse
- OneLake
- Fabric Data Factory pipelines and Dataflows Gen2
- Fabric Notebooks
- PySpark / Spark SQL
- Delta Lake
- Power BI, especially Direct Lake semantic modeling
- Strong SQL skills, including complex queries, stored procedures, and performance tuning.
- Strong Python/PySpark programming for distributed data processing.
- Experience with Fabric Real-Time Intelligence, Eventstream, KQL DB, or equivalent technologies such as Event Hubs/Stream Analytics.
- Understanding of dimensional modeling Star/Snowflake schema and Medallion Architecture.
- Familiarity with Git-based version control and CI/CD for Fabric deployment pipelines.
- Knowledge of Fabric security model workspace roles, item-level permissions, and OneLake data access.
- Prior experience with Azure Data Factory, Azure Synapse Analytics, or Azure Databricks.
Preferred Qualifications
- Microsoft Certified: DP-600 (Fabric Analytics Engineer Associate) or DP-203 (Azure Data Engineer Associate).
- Experience migrating workloads from Synapse/Databricks to Microsoft Fabric.
- Exposure to Microsoft Purview for data governance and lineage.
- Familiarity with Azure DevOps and Fabric Git integration.
- Experience with Semantic Link (SemPy) or Fabric data science/ML capabilities.
- Life Sciences domain experience is preferred.
Soft Skills
- Strong problem-solving skills and ability to independently troubleshoot complex pipeline issues.
- Positive communication skills for collaboration with architects, analysts, and business teams.
- Detail-oriented approach to data quality and reliability.
- Ability to adapt quickly to Microsoft's evolving Fabric feature set.
- Strong fundamentals in Spark, SQL, data modeling, and distributed data processing
📌 Data Engineer (Chennai)
🏢 Ignitho
📍 Chennai