Interim Head of Data | Inside IR35 | 6 months initial | Azure Databricks

Key responsibilities

• Lead a Data Engineering function of 20-30+ engineers and delivery partners.
• Own the engineering strategy and delivery of enterprise data products.
• Establish and embed a modern DataOps operating model covering CI/CD, testing, deployment, observability and release governance.
• Define engineering standards across ingestion, transformation, modelling, orchestration and data quality.
• Drive adoption of engineering best practice across internal and partner teams.
• Improve platform reliability, performance and cost efficiency.
• Lead technical governance and contribute to the Data Technical Design Authority.
• Develop the engineering capability, structure and technical career framework.
• Support the evolution of data applications, AI/ML and agentic workloads into governed production environments.
• You'll be an experienced Data Engineering leader with strong hands-on technical credibility and experience operating Databricks at enterprise scale.

Key experience includes:

• Leadership of large-scale Data Engineering functions, including internal teams, third-party and offshore delivery partners.
• Strong technical credibility across Databricks and Azure, with experience overseeing enterprise-scale Lakehouse environments.
• A track record of defining and implementing a Data Engineering strategy and operating model.
• Proven experience establishing and embedding DataOps, engineering standards and best practices across multiple delivery teams.
• Ownership of engineering governance, quality, reliability and technical assurance across a complex data estate.
• Experience improving engineering maturity across CI/CD, automated testing, observability, data quality and release management.
• Strong understanding of data architecture, ingestion, transformation, modelling and orchestration at enterprise scale.
• Experience managing cost, performance and technical debt, with clear accountability for engineering outcomes.
• Strong vendor and partner management, ensuring third parties deliver against defined engineering and quality standards.
• Experience building and developing high-performing engineering teams, including organisational design, capability development and career frameworks.
• Credibility with CDO/CIO-level stakeholders, with the ability to translate technical challenges, risks and investment decisions into business terms.

Experience with Databricks Apps, Lakebase, Genie, MLflow, Mosaic AI, Vector Search, RAG or agentic AI would be highly desirable but is not essential.

This is an opportunity to take ownership of a significant enterprise Data Engineering capability, establish the standards and operating model for the function, and shape how the organisation delivers modern data and AI solutions at scale.