Data Platform Engineer

Job summary

Build and operate the governed Microsoft Fabric data platform supporting reporting, analytics, and AI, integrating business and product data into a reliable, secure, and affordable source of truth. Responsibilities include developing lakehouses, warehouses, pipelines, and layered data models; automating quality checks and deployments; managing capacity and costs; implementing privacy and access controls; and preparing governed data for AI through retrieval, vector search, and controlled agent access.

Qualifications

• 6+ years of experience in Data Engineering, including at least two years working with Microsoft Fabric, Azure Synapse, or Databricks.
• Strong experience with SQL and Python or PySpark, and data modelling for analytics.
• Hands-on experience with Microsoft Fabric, including Lakehouse, Warehouse, Pipelines, Notebooks, and OneLake.
• Experience integrating ERP and CRM data, ideally from Dynamics 365 Finance & Operations and Dataverse.
• Experience with master data management and entity resolution, including matching customers and products across multiple systems.
• Practical experience implementing security and governance, including Entra ID, row-level security, and Microsoft Purview.
• Experience managing capacity and costs on a shared analytics platform.
• Strong proficiency in SQL and Python/PySpark development.
• Expertise in analytics-focused data modelling and data architecture.
• Experience with source control and CI/CD practices for data platforms, including Git and Azure DevOps.

Responsibilities

• Design, build and run the Microsoft Fabric platform: lakehouses, warehouses, pipelines and dataflows
• Ingest and integrate data from Dynamics 365, CRM, Kantata, licensing and telemetry
• Build a layered, medallion-style data model that turns raw data into certified, reusable datasets
• Resolve customer and product identities across CRM, ERP, licensing and telemetry
• Monitor pipelines and automate data quality tests for completeness, freshness, uniqueness and referential integrity
• Manage platform capacity and cost, separating workloads and scaling only when justified
• Run source control, environments and deployment pipelines for data and BI content
• Implement access, classification, row- and column-level security, masking and privacy controls with Information Security
• Maintain lineage and documentation
• Set engineering standards and coach BI developers and analysts in using them
• Prepare data for AI, exposing governed, well-described data to agents and models through controlled interfaces

Skills

Certifications

Industry

Company size

Relocation