Principal Data Engineer in Manchester

Help shape the next chapter of our data capability. We’re looking for a hands‑on Lead Data Engineer to own our data platforms: dependable, well‑modelled and easy to build on. This is an opportunity to own the data platforms that power analytics across the business and give a small team the tools to do its best work.

What You'll Be Responsible For

• As the Lead Data Engineer, you’ll own all data platforms while staying hands‑on in the code. You’ll work alongside Engineering and AI/Data Science, lead and develop the data team, and turn business needs into a realistic plan
• Build and run ingestion, orchestration, the BigQuery warehouse, DBT tooling and Python data services on GCP, including CI/CD, infrastructure as code, access and cloud costs
• Work alongside Engineering and AI/Data Science to provide the data they need, and fix data issues at source rather than patching them in the warehouse
• Turn business and analytics requests into one prioritised backlog and roadmap
• Review SQL, Python and dbt code, keep tests and contracts strong, and make every production change testable and reversible
• Lead and develop the team

Skills and Experience

• Strong SQL and Python, building tested, maintainable production pipelines and data services.
• Hands‑on experience with a cloud data warehouse, dbt (or equivalent) and workflow orchestration, including incremental loading, safe reruns and performance tuning.
• Data modelling for analytics and BI: dimensional design, semantic layers and well‑documented shared datasets.
• Ownership of a production data platform on a major cloud: deployment, infrastructure as code, permissions, monitoring, incident recovery and cost.
• Sound security and data privacy practice, including GDPR and least‑privilege access.
• Turning stakeholder needs into a prioritised backlog, and reconciling figures where finance reporting depends on them.
• Line management or mentoring of engineers, and technical leadership through code review and clear documentation.
• Tech stack includes GCP, BigQuery, DBT, Python and Terraform.
• GCP experience with BigQuery, Composer and Cloud Run or Cloud Functions, including cost optimisation.
• Airbyte or similar ingestion tools
• CDC, Kubernetes and data observability tools such as Elementary.
• Working with data scientists to put ML models or pipelines into production.

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