Primus Connect
Head of Data Engineering
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Head of Data Engineering – Databricks / Azure
Location: Leeds – Hybrid
Contract: Inside IR35
Rate: £750 - £850
Reporting to: CDO
Team: 20–30+
We’re recruiting for a Head of Data Engineering to lead and develop a large-scale data engineering function within a major enterprise environment.
This is a senior leadership role with responsibility for the engineering strategy, delivery standards, DataOps operating model, and overall quality of the organisation’s data products, built predominantly across the Azure Databricks Lakehouse.
You’ll lead internal engineering teams and third-party delivery partners, establishing the standards, governance, and assurance needed to create a scalable, reliable, and cost-efficient data estate.
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.
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What we’re looking for
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.
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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.
To be considered please click apply.
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