Staff Analytics Engineer
About the role
Staff Analytics Engineer - Borrowing
Our Borrowing Analytics Engineering Team
Our mission in Borrowing is to help people achieve their financial goals through better borrowing. Our customers borrow money to achieve something in their lives — whether that’s making a big life event affordable, buying something they need now without affecting their monthly budget, or getting by until payday. We’re shaping this mission by building products our customers love, while safely scaling some of Monzo’s biggest revenue lines.
Borrowing is one of Monzo’s most complex and fastest-growing domains. We operate 12+ products across multiple geographies, underpinned by 1,700+ data models and an analytics engineering team that’s scaling to match. We’re in the middle of a major data architecture transformation, expanding into new markets, and building the next generation of data infrastructure to support it all.
We’re looking for a Staff Analytics Engineer to help shape how Borrowing builds and uses data at scale. Reporting to the Borrowing Data Director, you’ll work across product, credit, engineering, Data Platform, and analytics engineering teams to turn complex technical problems into clearer systems, stronger data products, and better business decisions.
Key Responsibilities
- Architecting Borrowing’s data layer at scale. Partnering across Analytics Engineering, Product, Engineering, Credit, and Data Platform to shape how 1,700+ models across 12+ products are structured, connected, and evolved. You’ll set shared patterns that help teams build trusted, consistent, and scalable data products across Borrowing.
- Designing and governing data products. Moving us beyond ad-hoc tables toward well-defined, contractual data assets with clear ownership, SLAs, documentation, and interfaces. You’ll work with teams across Borrowing and Data Platform to define what makes a Borrowing dataset “production-grade” and consumable by analytics, ML, decisioning, and regulatory teams.
- Building feature stores and reusable analytical assets. Identifying cross-product signals (credit behaviour, repayment patterns, affordability, risk indicators) that should be modelled once, tested rigorously, and consumed by many. You’ll design the layer that turns raw product data into curated, versioned features that power models, dashboards, and decisions.
- Scaling our analytics engineering infrastructure. Shaping the tooling, patterns, and developer experience that make an 80+ person credit and data organisation more productive. This means influencing our data architecture and ways of working across data and credit disciplines.