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Jobs / Monzo

Staff Analytics Engineer

Monzo·Cardiffsenior

In short

  • →Ingeniero de análisis de datos de alto nivel en el área de préstamos de Monzo, construyendo infraestructura de datos a escala.
  • →Diseña y gestiona productos de datos confiables para múltiples productos y mercados, con enfoque en calidad, documentación y reutilización.
  • →Destacado: liderazgo clave en la transformación arquitectónica de datos en un dominio crítico y en crecimiento rápido.

Fluent English required for collaboration across global teams.

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What they ask for

  • ✓Experiencia sólida en ingeniería de datos a escala.
  • ✓Habilidad para arquitectar y escalar capas de datos en entornos complejos.
  • ✓Impulso para definir estándares y contratos de datos consistentes.
  • ✓Colaboración efectiva con equipos de producto, crédito, ingeniería y plataformas de datos.
  • ✓Capacidad para construir activos analíticos reutilizables (como feature stores).
  • ✓Experiencia trabajando con 1000+ modelos de datos y múltiples productos en mercado.

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🚀 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 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! 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 | 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 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)

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