OakNorth's Lending business runs on data — from credit decisions to portfolio reporting to operational oversight. This role exists to bring structure and reliability to that data layer. You will own the migration of our data models to dbt, build the dashboards that Finance and Lending Ops depend on, and set a higher standard for how data is modelled and consumed across the Lending business.

### Key responsibilities

Own the end-to-end migration of existing data models into dbt, delivering production-ready, well-documented, and tested models
Design and maintain a clean, scalable data layer in BigQuery that serves Finance, Lending Ops, and Engineering stakeholders
Build dashboards and self-service reporting tools that enable stakeholders to answer their own questions without engineering involvement
Define and enforce data modelling standards and conventions within the Lending data domain
Partner with Finance and Lending Ops to understand reporting requirements and translate them into reliable, reusable data assets
Write Python scripts and tooling to support data pipelines, transformations, and automation where needed
Use AI-assisted development practices to accelerate delivery and maintain high code quality
Work iteratively — ship early, gather feedback, and improve; you are comfortable with ambiguity and energised by a high pace of change

### What success looks like

All priority Lending data models have been migrated to dbt and are running reliably in production
Finance and Lending Ops stakeholders are actively using dashboards built by this role — with low dependency on ad-hoc requests to Engineering
A clear, documented data model structure exists for the Lending domain that other engineers can build on
Data quality issues are caught at the model layer, not discovered by stakeholders in reports

### Requirements

Proven experience as an Analytics Engineer or Data Engineer with strong dbt skills (model design, testing, documentation, refactoring)
Hands-on experience with Google Cloud Platform and BigQuery
Comfortable writing Python for data transformation, pipeline tooling, or automation
Strong instinct for data modelling — you think in schemas, not just queries
Experience building dashboards and self-service reporting tools for non-technical stakeholders
Solid grounding in software engineering principles: version control, code review, testing, and CI/CD
Comfortable working in agile teams — you work well in short cycles, prioritise ruthlessly, and adapt quickly
Comfortable working in a regulated financial services environment with appropriate data governance awareness

### Nice to haves

Experience in lending, credit, or financial services data domains
Familiarity with Looker, Metabase, or similar BI tools
Exposure to dbt Cloud and CI/CD for data pipelines

Data Analytics Engineer - 6 months FTC

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