Manager- Data Engineer (Azure, Fabric)

Key responsibilities:

• Design, implement, and optimize end-to-end ETL pipelines in Microsoft Fabric, from ingestion through multi-stage transformations to data loading and delivery.
• Build pipelines and notebooks using Python and PySpark; implement data validation, error handling, and quality controls.
• Collaborate with business analysts and stakeholders to translate requirements and accounting logic into transformation rules and data solutions.
• Work with data architects to design schemas and data models (star/snowflake) and, where needed, OLAP cubes aligned to application requirements.
• Ensure efficient, accurate processing from source systems into Fabrics data layers; optimize performance and scalability (partitioning, indexing, resource tuning).
• Leverage modern tooling and practices (e.g., Azure DevOps for boards, repos, CI/CD); uphold consistent development standards across the global team.
• Conduct unit, integration, and end-to-end testing; troubleshoot and continuously improve ETL processes.
• Maintain comprehensive, up-to-date documentation (processes, sources, data flows, models) accessible to stakeholders; ensure compliance with policies and standards.

Required skills and experience:

• High proficiency with Microsoft Fabric ETL; experience with related tools such as Azure Data Factory.
• Strong SQL for extraction, transformation, and querying; hands-on with SQL Server, Azure SQL Database, and Synapse Analytics.
• Data engineering fundamentals: data modeling and schema design (star/snowflake), transformation, and optimization for warehousing/analytics; experience with OLAP where applicable.
• Proficiency in Python and PySpark for ETL development within Fabric notebooks and pipelines.
• Experience loading and optimizing data at scale in Fabric and prior exposure to Azure Synapse and Azure Data Lake.
• Familiarity with Azure DevOps workflows (work tracking, version control, pipelines) and modern development practices.
• Rigorous testing approach (unit, integration, E2E), with robust data validation and error-handling procedures.
• Strong collaboration and communication in globally distributed teams; ability to share best practices and evolve standards based on feedback and industry trends.