Head of Data Engineering (London, ENG)
Posted Updated
Summary
9-month contract leading an enterprise data engineering function built on Microsoft Fabric: owning the data architecture roadmap (OneLake, Delta Lake medallion), integrations with platforms like Salesforce, Workday and Dynamics 365 F&O, governance via Purview/Entra ID, and DevOps practices, while managing a multidisciplinary engineering team. 2 days per week onsite in London at £675/day outside IR
Head of Data Engineering – Microsoft Fabric
Contract & Location: 9 month contract - 2 days per week in London office.
Rate: £675/day Outside IR35
Role:
- Leadership & Strategy: Lead the Data Engineering function and own the enterprise data engineering strategy, architecture roadmap, standards, and delivery across a Microsoft Fabric-led data estate.
- Microsoft Fabric Expertise: Provide senior technical leadership across Microsoft Fabric, including OneLake, Lakehouses, Warehouses, Data Pipelines, Dataflows Gen2, Spark Notebooks, KQL and Real-Time Intelligence.
- Data Architecture & Migration: Design and oversee modern cloud-first data architectures, including Delta Lake and Bronze/Silver/Gold medallion patterns, with experience migrating legacy platforms such as Oracle or Azure Synapse.
- Enterprise Data Integration: Lead the integration and data engineering strategy across key enterprise platforms including Salesforce, Certinia, Workday, Dynamics 365 F&O, GMS, ECS, Avetta, Rydoo and Tipalti.
- Engineering Team Leadership: Lead and develop a multidisciplinary engineering team covering development, testing, release management and platform operations, establishing strong engineering standards, code reviews and agile delivery practices.
- Governance, Security & Quality: Own data governance, security and lineage using technologies such as Microsoft Purview, Entra ID and RBAC, ensuring robust controls, data quality and compliance.
- DevOps & Operational Excellence: Establish CI/CD, Git-based source control, automated deployment and infrastructure-as-code practices, while optimising Spark workloads, Fabric capacity, performance and cost.