Gurgaon · Senior
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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 • 5+ 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.
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 • 5+ 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.
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 • 5+ 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.
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