What You Will Do

• Design, build, test, deploy, and support scalable batch and near-real-time data pipelines for structured, semi-structured, and unstructured data using Microsoft Fabric, Azure Data Factory, SSIS, SQL Server, Azure SQL, cloud storage, and other enterprise data sources.
• Develop modern data engineering solutions using Microsoft Fabric capabilities such as Lakehouse, Warehouse, OneLake, Copy Jobs, Notebooks, Dataflow Gen2, and related services, applying layered bronze/silver/gold architecture patterns where appropriate.
• Develop robust, reusable ETL/ELT and data transformation solutions using T-SQL, Python, PySpark, and Spark SQL, including incremental loading, Change Data Capture (CDC), schema evolution, reconciliation, restart/recovery, idempotency, and error-handling patterns.
• Design dimensional, relational, and Lakehouse data models that support analytics, operational and regulatory reporting, self-service analytics, and downstream system integrations.
• Ensure production reliability by implementing monitoring, alerting, logging, data quality controls, reconciliation, metadata, lineage, auditability, and operational support procedures.
• Optimize SQL, Spark, pipeline, and storage workloads for performance, scalability, reliability, and cost efficiency while applying security and privacy best practices, including least-privilege access, managed identities, service principals, secrets management, secure connectivity, and environment separation.
• Implement source control, automated deployment, and CI/CD practices using Git, Azure DevOps, and Microsoft Fabric deployment capabilities, and participate in code reviews, testing, release planning, incident resolution, root-cause analysis, and post-implementation reviews.
• Develop reusable engineering standards, frameworks, templates, patterns, and technical documentation to improve consistency, maintainability, and operational support across the data platform.
• Partner with Power BI developers, analysts, business stakeholders, Data Governance, and other technology teams to translate business requirements into trusted, well-structured data solutions and clearly communicate dependencies, risks, and technical trade-offs.
• Support the modernization and migration of legacy SSIS, SSRS, Azure Data Factory, SQL-based, and other data integration workloads to strategic cloud and Microsoft Fabric platforms.

What You Will Do

• Design, build, test, deploy, and support scalable batch and near-real-time data pipelines for structured, semi-structured, and unstructured data using Microsoft Fabric, Azure Data Factory, SSIS, SQL Server, Azure SQL, cloud storage, and other enterprise data sources.
• Develop modern data engineering solutions using Microsoft Fabric capabilities such as Lakehouse, Warehouse, OneLake, Copy Jobs, Notebooks, Dataflow Gen2, and related services, applying layered bronze/silver/gold architecture patterns where appropriate.
• Develop robust, reusable ETL/ELT and data transformation solutions using T-SQL, Python, PySpark, and Spark SQL, including incremental loading, Change Data Capture (CDC), schema evolution, reconciliation, restart/recovery, idempotency, and error-handling patterns.
• Design dimensional, relational, and Lakehouse data models that support analytics, operational and regulatory reporting, self-service analytics, and downstream system integrations.
• Ensure production reliability by implementing monitoring, alerting, logging, data quality controls, reconciliation, metadata, lineage, auditability, and operational support procedures.
• Optimize SQL, Spark, pipeline, and storage workloads for performance, scalability, reliability, and cost efficiency while applying security and privacy best practices, including least-privilege access, managed identities, service principals, secrets management, secure connectivity, and environment separation.
• Implement source control, automated deployment, and CI/CD practices using Git, Azure DevOps, and Microsoft Fabric deployment capabilities, and participate in code reviews, testing, release planning, incident resolution, root-cause analysis, and post-implementation reviews.
• Develop reusable engineering standards, frameworks, templates, patterns, and technical documentation to improve consistency, maintainability, and operational support across the data platform.
• Partner with Power BI developers, analysts, business stakeholders, Data Governance, and other technology teams to translate business requirements into trusted, well-structured data solutions and clearly communicate dependencies, risks, and technical trade-offs.
• Support the modernization and migration of legacy SSIS, SSRS, Azure Data Factory, SQL-based, and other data integration workloads to strategic cloud and Microsoft Fabric platforms.