Build and own the data layer
• Design, build and run scalable data pipelines in Azure Databricks (SQL/PySpark) on ADLS, orchestrated with Azure Data Factory, integrating multiple ERP and operational source systems.
• Design, maintain and optimize Azure Analysis Services models and Power BI / Fabric semantic models, moving business logic upstream into the data platform where it improves performance and maintainability.
• Work with Product Owners and Reporting Analysts to turn requirements into governed data products, dashboards and reports.
• Own the monitoring and refresh of scheduled pipelines and models, including root-cause analysis when something fails.
• Maintain runbooks, documentation and lineage so the landscape can be supported by more than one person.
• Apply Git-based version control, code review and dev-to-prod promotion, and uphold data quality and governance standards.
• Use AI assistants and agents in everyday engineering work, for example for code, testing and documentation, and help make our data AI-ready.
• Build reusable components and automation that reduce manual effort and scale the reporting landscape.
• Introduce and champion new technologies and practices that improve efficiency, scalability and cost.
• Coach Reporting Analysts and new colleagues on engineering good practice.
• Share knowledge openly, so that no capability depends on a single person.