Databricks Data Architect

Data Architect – Databricks / Azure Data Platform Location: London (Hybrid) Rate: £flexible day rate Duration: Initial 6 months until end of March 2027 Robert Half are supporting a leading property and real estate organisation on a major Finance Data Lake and Self-Service Reporting transformation programme and are seeking an experienced Databricks Data Architect to join the team. This is a highly technical, hands-on role requiring deep expertise across the Databricks ecosystem. We are specifically looking for candidates with experience designing and implementing reusable Databricks frameworks, metadata-driven architectures and enterprise-scale data platforms. Essential Experience • Strong hands-on Databricks Architecture experience • Azure Data Platform expertise (ADLS, ADF, Synapse, Databricks) • Dynamics 365 Finance & Operations (D365 F&O) experience • Expert Data Modelling skills (Conceptual, Logical, Physical, Dimensional) • Databricks Data Engineering experience using PySpark • Delta Lake and Medallion Architecture • Delta Live Tables (DLT) • Unity Catalog implementation and governance • Databricks Workflows and orchestration • Metadata-driven ingestion and transformation frameworks • Databricks Asset Bundles (DABs) • Databricks Autoloader • Enterprise Data Lake and Lakehouse Architecture • DevOps and CI/CD within Databricks environments • Azure DevOps and Git-based deployment pipelines • Strong stakeholder engagement and solution design capability Highly Desirable • Experience working for specialist Databricks consultancies or Databricks partners • Experience implementing Databricks Solution Accelerators • MLflow • Mosaic AI • Agentic AI / GenAI enablement on Databricks • Microsoft Fabric exposure • Finance Data Lake programmes • Real Estate, Property or Financial Services experience What You'll Be Doing • Defining Data Architecture and Data Modelling standards • Designing scalable Databricks Lakehouse solutions • Implementing reusable data engineering frameworks and accelerators • Building metadata-driven ingestion and orchestration patterns • Supporting self-service analytics and reporting capabilities • Working closely with Data Engineers, Architects and business stakeholders • Ensuring data governance, quality and scalability across the platform • Helping shape future AI-enabled reporting and analytics capabilities