[All remote jobs](https://jobicy.com/jobs.md)[![Monzo logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/5b4c0ec3-221.jpg)](https://jobicy.com/company/monzo.md)[Monzo](https://jobicy.com/company/monzo.md) # Staff Analytics Engineer Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/monzo.md)ShareRemote from[UK](https://jobicy.com/job-region/uk.md)SalaryGBP 121,600–164,600 / yrDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceDirectorPublished3 Nov 2026Apply before3 Dec 2026Listing views53Application actions4Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Monzo is hiring a Staff Analytics Engineer to define and scale the data architecture for its Borrowing domain. The role governs trusted, reusable data products across more than 1,700 models and 12+ lending products, including feature layers for analytics, ML, decisioning, and regulatory use cases. It requires deep expertise in dbt, cloud data warehousing, CI/CD, testing, orchestration, schema contracts, and data-product governance. The successful candidate will lead through technical influence, partnering closely with product, credit, backend engineering, data platform, and senior leadership rather than managing people directly. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a staff-level architecture role in a complex, regulated lending environment with broad technical and organizational influence. Success depends on resolving ambiguous cross-domain trade-offs while establishing scalable standards used by many teams. ## Salary analysis Estimated compensation compared with the broader UK market for similar roles. Estimated job median (per year)Highly competitive£143,100UK market range (per year)£110k–£170k0£187.00k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses a £121,600–£164,600 range. The annual cadence is inferred from the compensation level, seniority, UK location, and full-time role context rather than explicitly stated. A reasonable estimated UK annual market range for a staff-level analytics engineering role in financial services is £110,000–£170,000. ## Core skills Skills and capabilities most closely associated with this opportunity. [Analytics engineering](https://jobicy.com/jobs?search_keywords=Analytics%20engineering.md)[Data architecture](https://jobicy.com/jobs?search_keywords=Data%20architecture.md)[dbt](https://jobicy.com/jobs?search_keywords=dbt.md)[BigQuery](https://jobicy.com/jobs?search_keywords=BigQuery.md)[Data product governance](https://jobicy.com/jobs?search_keywords=Data%20product%20governance.md)[Feature store design](https://jobicy.com/jobs?search_keywords=Feature%20store%20design.md)[Data modeling](https://jobicy.com/jobs?search_keywords=Data%20modeling.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[CI/CD for data](https://jobicy.com/jobs?search_keywords=CICD%20for%20data.md)[Data testing](https://jobicy.com/jobs?search_keywords=Data%20testing.md)[Workflow orchestration](https://jobicy.com/jobs?search_keywords=Workflow%20orchestration.md)[Schema contracts](https://jobicy.com/jobs?search_keywords=Schema%20contracts.md)[Data documentation](https://jobicy.com/jobs?search_keywords=Data%20documentation.md)[Credit risk analytics](https://jobicy.com/jobs?search_keywords=Credit%20risk%20analytics.md)[Stakeholder management](https://jobicy.com/jobs?search_keywords=Stakeholder%20management.md) Sample interview questionsHow would you approach standardising data models across multiple borrowing products while preserving domain-specific needs?I would first map shared business entities, metrics, and source-system semantics, then define a layered model with common canonical concepts and clearly bounded product-specific extensions. I would introduce versioned contracts, ownership, tests, documentation, and an adoption plan that prioritises high-value shared models without forcing premature standardisation. Describe how you would turn an existing dataset into a production-grade data product. I would define its consumers and decisions supported, assign an accountable owner, document schema and business meaning, establish freshness and quality SLAs, and implement automated tests and observability. I would also define access patterns, versioning and change-management expectations so downstream users can rely on it safely. What trade-offs would you consider when designing a reusable feature layer for credit decisioning and machine learning? I would evaluate correctness, point-in-time validity, freshness, granularity, lineage, privacy, cost, and online versus batch serving needs. The design should prevent leakage, make definitions reusable across consumers, and provide transparent versioning so regulatory reporting and model governance remain auditable. How would you influence engineering and platform teams when you do not have direct management responsibility? I would build alignment around concrete user problems and measurable outcomes, present clear architectural options with trade-offs, and create lightweight standards that make the preferred path easier to adopt. Design reviews, reference implementations, documentation, and collaborative roadmaps help turn influence into durable leverage. How do you decide whether a data workflow should use streaming or batch processing? I begin with the business decision's latency requirement and then assess source reliability, event ordering, operational complexity, cost, and data-quality controls. Streaming is appropriate when near-real-time action creates material value, while batch is usually preferable when daily or periodic freshness meets the need more reliably and economically. Opportunity details ## About this role. 🚀 We’re on a mission to make money work for everyone. We’re 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](https://monzo.com/current-account/personal) and [business](https://monzo.com/business-banking) bank accounts, we offer [joint accounts](https://monzo.com/current-account/joint-account), [accounts for 16-17 year olds](https://monzo.com/current-account/16-17), a [free kids account](https://monzo.com/current-account/under-16s) and [credit cards](https://monzo.com/flex) in the UK, with more exciting things to come beyond. Our UK customers can also [save](https://monzo.com/savings), [invest](https://monzo.com/investments)and [combine their pensions](https://monzo.com/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! We’re not about selling products – we want to solve problems and change lives through Monzo ❤️ Staff Analytics Engineer – Borrowing 📍London/Cardiff/UK Remote | 💰 £121,600-164,600 + Incentive Awards tied to your performance and[benefits](https://monzo.com/careers/) |[Hear from the team](https://www.youtube.com/watch?v=U62l09nB0M4) ✨ ⭐ 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. 🔑 You’ll play a key role by… * 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, while partnering with the central Data Platform team to ensure Borrowing’s 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. You’ll 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 Borrowing’s 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 what’s possible and what’s next. * Leading through influence and leverage. You won’t manage people directly, but you’ll shape how an entire domain builds data. You’ll multiply the impact of AEs across Borrowing by setting the right patterns, unblocking architectural decisions, and raising the bar on what good looks like. 🤩 We’d love to hear from you if… * You think in systems, not just queries. You’ve 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. * You’ve built data products, not just data models. You understand the difference between a table that exists and a data asset that’s governed, documented, versioned, discoverable, and trusted. You’ve defined SLAs, contracts, interfaces, or ownership models for data consumers, and you’re 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 don’t just use these tools, you shape how teams use them. You’ve hit the scaling limits and know what to do about them. * You can design reusable feature layers. You’ve 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. * You’re 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 don’t 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. You’re 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. You’re 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. What’s in it for you: 💰 £121,600-164,600 + Incentive Awards tied to your performance and[benefits](https://monzo.com/careers/) ✈️ 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 benefits[here](https://monzo.com/careers/) #LI-AS #LI-Remote Equal opportunities for everyone Diversity and inclusion are a priority for us and we’re making sure we have lots of support for all of our people to grow at Monzo. At Monzo, we’re embracing diversity by fostering an inclusive environment for all people to do the best work of their lives with us. This is integral to our mission of making money work for everyone. You can read more in our [blog](https://monzo.com/blog/diversity-and-inclusion), 2026 [Diversity and Inclusion Report](https://monzo.com/blog/our-2026-diversity-and-inclusion-report) and 2025 [Gender Pay Gap Report.](https://monzo.com/blog/gender-pay-gap-report-2025) We’re an equal opportunity employer. All applicants will be considered for employment without attention to age, ethnicity, religion, sex, sexual orientation, gender identity, family or parental status, national origin, or veteran, neurodiversity or disability status. If you have a preferred name, please use it to apply. We don’t need full or birth names at application stage 😊 Show more [Apply now >](https://jobicy.com/jobs/158630-staff-analytics-engineer-3.md) * ![Upload CV](data:image/svg+xml;base64,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) ### Upload your resume now To unlock remote work opportunities and be discovered by global employers. 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