Data Engineer

Trusted finance decisions depend on data that arrives complete, on time, and ready to use. You will build the pipelines that turn vehicle, contract, and service data into a reliable foundation for credit, pricing, and customer decisions. This role matters because every delayed feed or broken model slows the people who put customers into the right vehicle, on the right terms.

Performance Objectives

• Deliver production data pipelines that land core finance, vehicle, and service datasets within agreed service levels, with documented lineage and failed-run recovery under four hours.
• Cut manual reconciliation by automating quality checks so business users can trust daily figures on originations, arrears, and contract performance.
• Stand up reusable data products—curated tables and APIs—that analytics and risk teams can query without custom extracts.
• Reduce pipeline incidents by designing idempotent loads, clear alerts, and runbooks that on-call engineers can execute without tribal knowledge.
• Partner with product and risk owners to translate a decision (price, approve, service) into a data contract with owners, freshness, and acceptance tests.
• Improve platform cost and runtime by retiring redundant jobs and right-sizing compute against measured workload, not estimates.
• Leave every major dataset with tests, a data owner, and a short guide a new analyst can follow in a day.

Environment & Resources

You join a small data engineering group inside automotive financial services, working with risk, finance, and digital product teams. You will use a modern cloud warehouse, orchestration, and version-controlled pipelines, with access to source systems for contracts, vehicles, and servicing. You report to the data platform lead and influence backlog with analytics and engineering partners. Budget for tools and cloud spend is set annually; you help spend it where reliability and speed improve.

Essential Qualifications

• Proven delivery of batch or streaming pipelines in production, including schema change, backfill, and incident response.
• Strong SQL and at least one engineering language used to build tested, reviewed data jobs.
• Hands-on experience modelling financial or operational data—contracts, payments, assets, or events—so numbers reconcile to a source of truth.
• Practical use of orchestration, CI, and data-quality checks, not notebooks alone.
• Clear written specs: you have defined SLAs, owners, and acceptance criteria with non-technical partners.
• Comfort working in a regulated consumer or commercial finance setting, including access control and audit-friendly change history.

Role Selling Points

• Work close to decisions that fund vehicles and keep them on the road—not abstract dashboards with no owner.
• Shape data products while the platform is still small enough that your design choices stick.
• Balance engineering craft with domain depth in automotive finance, servicing, and insurance-linked products.
• A public, late-stage organisation with the scale to invest in data and the focus of a specialist finance business.

Call to Action

If you have shipped data others stake decisions on, apply with a short note on a pipeline you owned and the outcome it changed. We read for evidence of delivery, not keyword volume.