lupapets - Analytics Engineer
Requirements
• 2–3 years of hands-on experience in an analytics engineering, SQL engineering, or BI engineering role • Exceptional SQL skills, you write complex queries fluently, understand query optimisation, and care deeply about data model structure and readability • Solid experience with data modelling principles and building scalable, well-tested analytics layers (dbt experience strongly preferred) • Comfortable working with large, messy datasets and turning them into something clean, reliable, and trustworthy • Experience in a B2B SaaS, health-tech, or fast-growth startup environment (nice to have) • Familiarity with BI tooling such as Looker, Metabase, or similar (nice to have) • As a person, you: • Are proactive and self-structured, you create order in chaos and don’t need a rigid process to do great work • Communicate clearly and confidently with non-technical stakeholders, you can explain a data model to a sales lead without losing them • Take ownership of data quality as a first principle, if something looks wrong, you investigate and fix it, you don’t just flag it • Thrive in a fast-moving environment where the data landscape is constantly evolving as the product grows • Are excited to work in-person from our Paddington, London HQ • Value working with people who are kind, ambitious, and pragmatic • What does success look like in 6 months? • You own the analytics layer with confidence, models are clean, documented, tested, and trusted across the business • Stakeholders come to you first when they need to understand what’s happening in the data, because they know you’ll give them a straight answer • You’ve made a visible impact on how the business uses data to make decisions
Responsibilities
• Own the analytics layer of our data platform; writing, structuring, and maintaining the SQL models that power dashboards, product metrics, and business intelligence across Lupa • Transform raw data from our platform into clean, reliable, well-documented datasets that Product, Engineering, and Commercial teams can actually use • Build and maintain reports that give the business a clear picture of clinical outcomes, practice performance, customer health, and growth metrics • Define and enforce data modelling best practices, including naming conventions, documentation standards, and testing coverage • Partner closely with the wider Data team to ensure analytics models sit on top of solid, well-engineered pipelines • Work directly with stakeholders across the business to understand what they need to know, translate that into clean SQL, and deliver answers that drive decisions
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