Data Engineer - Azure + Fabric

Data Engineer – Microsoft Fabric / OneLake

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.
• Collaborate with Solution Architects, Lead Data Engineers, BI teams, and business stakeholders to deliver scalable data solutions.

Required Experience & Skills

• 4+ years of overall Data Engineering experience with hands-on experience in Azure / Microsoft Fabric.
• Strong hands-on experience with Microsoft Fabric Lakehouse, OneLake, Warehouse, Data Pipelines, and Notebooks.
• Proficiency in Spark / PySpark, Python, SQL, and Delta Lake.
• Experience with ETL/ELT, CDC, data ingestion, transformation, and Medallion Architecture.
• Experience developing data quality, validation, reconciliation, and exception-handling frameworks.
• Knowledge of Power BI semantic models and Direct Lake.
• Experience with Microsoft Purview, Azure DevOps, Git, CI/CD, and deployment 
• Commercial insurance or brokerage data experience preferred.

Education & Certifications

• B.Tech / Bachelor’s degree or equivalent in Computer Science, Engineering, Information Technology, or a related field.
• Microsoft Fabric or Azure Data Engineer certification preferred.

Required Experience & Skills

• 4+ years of overall Data Engineering experience with hands-on experience in Azure / Microsoft Fabric.
• Strong hands-on experience with Microsoft Fabric Lakehouse, OneLake, Warehouse, Data Pipelines, and Notebooks.
• Proficiency in Spark / PySpark, Python, SQL, and Delta Lake.
• Experience with ETL/ELT, CDC, data ingestion, transformation, and Medallion Architecture.
• Experience developing data quality, validation, reconciliation, and exception-handling frameworks.
• Knowledge of Power BI semantic models and Direct Lake.
• Experience with Microsoft Purview, Azure DevOps, Git, CI/CD, and deployment 
• Commercial insurance or brokerage data experience preferred.

Education & Certifications

• B.Tech / Bachelor’s degree or equivalent in Computer Science, Engineering, Information Technology, or a related field.
• Microsoft Fabric or Azure Data Engineer certification preferred.

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.
• Collaborate with Solution Architects, Lead Data Engineers, BI teams, and business stakeholders to deliver scalable data solutions.