Fabric Senior Data Engineer
At a glance
Senior Data Engineering role at Fa Ewjt Saasfaprod1. Gurugram · full-time.
Pay not stated Data Engineering salary
dataroles summary, based on the employer's posting.
What you'll do
- Build and maintain Microsoft Fabric Lakehouse solutions, organizing data through Bronze, Silver and Gold layers
- Create reusable ingestion pipelines and CDC frameworks for GCP, spreadsheets, email files and SharePoint data
- Add audit trails, schema checks and failure handling to ingestion, then cleanse and validate data in Silver
- Shape Gold datasets with business measures, dimensions, aggregations and secure views for Power BI consumption
- Monitor data quality and governance, resolve pipeline or production problems, and coordinate delivery with technical and business teams
What you bring
- At least five years in data engineering, with practical Azure or Microsoft Fabric experience
- Hands-on work with Fabric Lakehouse, OneLake, Warehouse, Data Pipelines and Notebooks
- Strong skills in Spark or PySpark, Python, SQL and Delta Lake
- Experience building ingestion, CDC, transformation and data-quality frameworks using Medallion Architecture
Who this fits
This senior role suits an experienced data engineer who can build Fabric pipelines and Lakehouse layers while supporting Power BI models and Direct Lake use. The position is hybrid in Gurugram, India. Commercial insurance or brokerage data experience is preferred.
From the employer
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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