oracle
Lead Data Engineer - Azure + Fabric
EXL Talent Acquisition Team
Gurugram, IND
Full-time
Senior · 8+ years experience
Hybrid
Discovered 3 weeks ago
Microsoft FabricMicrosoft Fabric LakehouseNotebooksSparkPySparkPython
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Microsoft FabricMicrosoft Fabric LakehouseNotebooks
Role Overview
We are looking for a Data Engineer to develop and maintain a modern data platform leveraging Microsoft Fabric, OneLake, and the Medallion Architecture. The role will focus on building scalable data pipelines, implementing data transformation and quality processes, and supporting curated data products for analytics and Power BI.
Key Skills for This Role
Microsoft FabricMicrosoft Fabric LakehouseNotebooksSparkPySparkPython
Full Job Posting
Role Overview
We are looking for a Data Engineer to develop and maintain a modern data platform leveraging Microsoft Fabric, OneLake, and the Medallion Architecture. The role will focus on building scalable data pipelines, implementing data transformation and quality processes, and supporting curated data products for analytics and Power BI.
Key Responsibilities
- Develop and maintain Microsoft Fabric / OneLake Lakehouse solutions across Bronze, Silver, and Gold layers.
- Build reusable data ingestion pipelines and CDC frameworks for sources including GCP, CSV, Excel, email files, and SharePoint.
- Implement Bronze-layer ingestion with audit logging, schema validation, schema-drift detection, and error handling.
- Develop Silver-layer transformations for cleansing, standardization, data quality, validation, and exception handling.
- Build Gold-layer datasets including curated entities, conformed dimensions, business metrics, KPIs, aggregations, and secure views.
- Develop and optimize Spark / PySpark, Python, and SQL workloads for scalable data processing.
- Build data pipelines using Fabric Data Pipelines, Synapse Pipelines, and Notebooks.
- Support Power BI semantic models and Direct Lake data consumption requirements.
- Implement data quality checks, reconciliation processes, monitoring, and operational controls.
- Follow data governance and security standards, including Purview, RBAC, PII/PHI controls, classification, and lineage.
- Troubleshoot pipeline failures, performance issues, data discrepancies, and production incidents.
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