Lead Data Engineer

Lead Data Engineer

Hull, HU10 + 2 days home working

Up to £80,000 + Benefits

Your new role

I am currently recruiting for a Lead Data Engineer to build and strengthen the foundations of the data platform, delivering reliable pipelines, governed, high-quality data products that teams across Sales, Network, Customer Experience, Finance and Operations can trust.

Responsibilities

Build, optimise and operate ELT/ETL pipelines into our data platform using SQL and Python (PySpark), with a focus on reliability, performance and maintainability.

Develop and maintain core data models (curated layers, dimensional models, shared definitions) that enable consistent KPI reporting and analysis.

Implement and embed data quality controls (freshness, completeness, accuracy, reconciliation checks) and monitoring so issues are detected early and fixed at source where possible.

Partner with analysts and stakeholders to turn business questions into reusable, well-governed data products rather than one-off reporting.

Improve engineering standards: Git workflows, code review, documentation, repeatable deployments, and sensible environment separation.

Support governance by helping define data contracts, ownership, lineage and "what does this metric mean?" clarity, so teams can use and challenge the numbers confidently.

Contribute to the wider platform roadmap while keeping delivery outcomes front and centre.

Lead by example on engineering quality: set the bar for production-grade delivery (testing, monitoring, documentation, code review, release discipline) and help the team consistently meet it.

Coach and uplift others: mentor junior engineers/analysts, run pairing sessions, provide practical feedback, and help raise SQL/Python capability across the function.

Experience needed

Strong hands-on experience as a data engineer in complex, high-growth or technology-led organisations.

A track record of taking data pipelines and models from "fragile and fragmented" to "trusted, governed and embedded" through practical engineering improvements.

Solid experience across the data engineering lifecycle: ingestion, transformation/modelling, and enabling consumption through BI/semantic conventions.

Hands-on capability with modern cloud data platforms and tooling, and a clear view of what "good" looks like for testing, monitoring, environments and deployment.

Proven approach to data quality: not just fixing reports, but improving definitions, controls and root causes in upstream systems and processes.

Strong communication skills: able to explain trade-offs, risks, and delivery choices clearly to non-technical stakeholders, and comfortable being challenged.

A high-standards, low-ego working style: collaborative, pragmatic, and focused on outcomes that stick (not dashboards that nobody uses).

Must have developed a Data Platform from inception to completion.

Managed and developed data engineers, forming a high-performing team.

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