Staff Analytics Engineer
Monzo
| Company | Monzo |
| Category | Engineering |
| Location | Cardiff |
| Remote | Remote |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 24 Jun 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (greenhouse) |
Description
đ Weâre on a mission to make money work for everyone.
Weâre waving goodbye to the complicated and confusing ways of traditional banking.
After starting as a prepaid card, our product offering has grown a lot in the last 10 years in the UK. As well as personal and business bank accounts, we offer joint accounts , accounts for 16-17 year olds , a free kids account and credit cards in the UK, with more exciting things to come beyond. Our UK customers can also save , invest and combine their pensions with us.
With our hot coral cards and get-paid-early feature, combined with financial education on social media and our award winning customer service, we have a long history of creating magical moments for our customers!
Weâre not about selling products - we want to solve problems and change lives through Monzo â¤ď¸
Staff Analytics Engineer - Borrowing
đLondon/Cardiff/UK Remote | đ° ÂŁ121,600-164,600 + stock and benefits | Hear from the team â¨
â 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 â whether thatâs making a big life event affordable, buying something they need now without affecting their monthly budget, or getting by until payday. Weâre shaping this mission by building products our customers love, while safely scaling some of Monzoâs biggest revenue lines.
Borrowing is one of Monzoâs most complex and fastest-growing domains. We operate 12+ products across multiple geographies, underpinned by 1,700+ data models and an analytics engineering team thatâs scaling to match. Weâre 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.
Weâre looking for a Staff Analytics Engineer to help shape how Borrowing builds and uses data at scale. Reporting to the Borrowing Data Director, youâll 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.
đ Youâll play a key role byâŚ
Architecting Borrowingâs 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. Youâll 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. Youâll work with teams across Borrowing and Data Platform to define what makes a Borrowing dataset âproduction-gradeâ 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. Youâll 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, while partnering with the central Data Platform team to ensure Borrowingâs needs are reflected in ingestion, streaming, and schema contract design.
Driving cross-product data consistency . As we expand across geographies and product lines, ensuring our data models are cohere