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
• 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