• Develop and maintain data ingestion and transformation pipelines; • Work with Lakehouse architectures; • Work in GCP and Databricks environments; • Create and evolve data structures in BigQuery and Delta Lake; • Support the definition and application of dimensional and relational modeling (Star Schema, Snowflake, etc.); • Ensure data quality, consistency, and security; • Support performance and cloud cost monitoring activities; • Collaborate with business units and data teams.

• Architecting Borrowing’s data layer at scale. • Designing and governing data products. • Building feature stores and reusable analytical assets. • Scaling our analytics engineering infrastructure. • Driving cross-product data consistency. • Being a senior technical partner for Borrowing’s data estate. • Leading through influence and leverage.

Role Description 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 ❤️ About our Analytics Engineering Team: - Works in the intersection between data, engineering and our collectives - Money, Borrowing, Operations and Financial Crime and beyond. - Responsible for building downstream data models from backend services. - Aims to make our Data Warehouse a genuine competitive advantage for Monzo. - Supports decision making, Business Intelligence, key financial reconciliation processes and best in class analytics and Data Science. You'll be an individual contributor in our Analytics Engineering team, working across a variety of projects to: - Spot patterns in the way we build our Data Warehouse and optimise our BI platform, Looker. - Load and transform even more data. - Minimise our cloud costs. - Contribute using our best practices, keeping quality high. We are at an exciting stage in our growth and have roles available across Growth and Finance. Qualifications - Some experience and a passion for Data Modelling, ETL projects and Big Data as an engineer, developer or analyst. - Confidence with SQL and data modelling. - Comfortable with general Data Warehousing concepts. - Attention to detail. - Ready to be part of a growing team in new areas of growth! Requirements - Support the building of robust pipelines and data models downstream of backend services (mostly in BigQuery). - Build with optimisation of our Data Warehouse in mind, spotting and raising opportunities to reduce complexity and cost. - Help define and manage best practices for our Data Warehouse. - Follow our established best practices and standards defined by the team. - Investigate and effectively work with colleagues from other disciplines to monitor and improve data quality within the warehouse. Benefits - Salary: £57,800-£75,000. - Visa sponsorship available. - Flexible working hours. - £1,000 learning budget each year for books, training courses and conferences. - Work from home setup including Macbooks and additional support for remote workers. - Incentive Awards tied to your performance. - Plus lots more! Company Description 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. 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.

• Own the hardest modeling and architecture in your team's scope — ontology objects (types, properties, link types, and actions) that model your part of the business as it actually operates, and the dimensional and semantic models (e.g., Looker/LookML) that serve them downstream • Act as a technical thought partner to the product and business leaders your team supports: understand their goals deeply and translate ambiguous or conflicting business needs into clear, durable technical plans • Take end-to-end ownership of your team's most business-critical initiatives, where deep semantic and architectural judgment is the differentiator • Align your team's models with shared representations of core entities (customer, policy, claim) so they stay consistent and interoperable across the mesh — partnering with the Principal Engineer and peers where definitions are cross-cutting • Define the modeling patterns, naming conventions, and reference implementations your team builds on, and contribute them back to the discipline's shared standards • Drive data-as-a-product expectations within your team's scope — ownership, contracts, documentation, and reliability for what your team owns • Partner with domain data engineers to shape the data contracts and pipelines that feed clean, well-defined ontology objects, and surface upstream issues that degrade your team's models • Raise the technical bar through model and design review, pairing, mentorship, and contributions to hiring and onboarding • Set your team's patterns for applying Claude and Claude Code to analytics engineering work, and design the ontology and semantic layer to be AI-consumable so tools like Databricks Genie can reason over your team's data reliably