Staff Analytics Engineer

Staff Analytics Engineer - Borrowing

London/Cardiff/UK Remote | 121,600-164,600 + stock and benefits | Hear from the team

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 thats making a big life event affordable, buying something they need now without affecting their monthly budget, or getting by until payday. Were shaping this mission by building products our customers love, while safely scaling some of Monzos biggest revenue lines.

Borrowing is one of Monzos most complex and fastest-growing domains. We operate 12+ products across multiple geographies, underpinned by 1,700+ data models and an analytics engineering team thats scaling to match. Were 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.

Were looking for a Staff Analytics Engineer to help shape how Borrowing builds and uses data at scale. Reporting to the Borrowing Data Director, youll 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.

Youll play a key role by

• Architecting Borrowings 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. Youll 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. Youll 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. Youll 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 Borrowings 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 coherent and comparable. Youll work with AE leads and domain experts to define shared conventions and abstractions that allow us to reason about Borrowing as a whole, not just product-by-product.
• Being a senior technical partner for Borrowings data estate. Partnering with backend engineers on source data payload design, with product managers on measurement strategy, with credit teams on decisioning data, and with senior leadership on whats possible and whats next.
• Leading through influence and leverage. You wont manage people directly, but youll shape how an entire domain builds data. Youll multiply the impact of AEs across Borrowing by setting the right patterns, unblocking architectural decisions, and raising the bar on what good looks like.

Wed love to hear from you if

• You think in systems, not just queries. Youve designed data architectures that span multiple products or domains, and you know how to keep them coherent as they scale. You can take ambiguous problems and turn them into a clear technical direction, delivery sequence, and set of trade-offs.
• Youve built data products, not just data models. You understand the difference between a table that exists and a data asset thats governed, documented, versioned, discoverable, and trusted. Youve defined SLAs, contracts, interfaces, or ownership models for data consumers, and youre excited to do this at scale.
• You have deep fluency with analytics engineering systems and infrastructure. dbt at scale, BigQuery or equivalent, CI/CD for data, testing frameworks, and orchestration. You dont just use these tools, you shape how teams use them. Youve hit the scaling limits and know what to do about them.
• You can design reusable feature layers. Youve designed, contributed to, or have a clear vision for reusable feature layers that serve multiple consumers, including ML pipelines, dashboards, decisioning engines, and regulatory reporting. You understand the trade-offs between freshness, cost, granularity, correctness, and ease of use.
• Youre comfortable at the platform boundary. You can have a productive conversation with a Data Platform engineer about ingestion patterns, streaming vs. batch trade-offs, schema evolution, and infrastructure costs. You dont need to build the platform, but you need to shape what it delivers.
• You connect technical choices to business outcomes. You care about whether data models, feature layers, and platform patterns actually improve decisions. You understand how product, credit, engineering, and operational teams use data, and you use that context to align people around better technical choices.
• You lead through others. You create leverage not by writing more SQL, but by setting patterns, reviewing designs, unblocking teams, and raising the standard. Youre energised by making 30+ people more effective, not by being the single expert.
• You communicate across altitudes. You can whiteboard a data architecture with a back-end engineer in the morning and present a strategic data roadmap to a director in the afternoon. You adapt your message to your audience without losing precision.
• You care about credit products. Youre excited, or curious, about the complexity of lending, including risk, affordability, regulatory constraints, and multi-product dynamics. You see it as a fascinating data domain, not just a business vertical.

Interview process

Our typical process is structured as:

• Recruiter call
• Initial call
• 3 x 1 hours sessions (final interview)

Our average process takes around 3-4 weeks but we will always work around your availability.

Whats in it for you:

121,600-164,600 base

We can help you relocate to the UK

We can sponsor visas

This role can be based in our London office, but we're open to distributed working within the UK (with ad hoc meetings in London).

We offer flexible working hours and trust you to work enough hours to do your job well, at times that suit you and your team.

Learning budget of 1,000 a year for books, training courses and conferences

And much more, see our full list of benefitshere

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