Analytics Engineer / Data Engineer, ProblemShared

• Strong in Python (incl. PySpark) and SQL on a modern cloud lakehouse. Comfortable reading a query plan
• Hands-on dbt — you’ve authored models, written tests, and reviewed others’ models with metadata discipline
• A track record of taking semi-structured source data (APIs, JSON, denormalised exports) and turning it into models other people can build on without fear
• Comfortable disagreeing with a stakeholder or a teammate and still landing the decision together. You argue to learn what’s true/best, not to win
• A view on when AI-supported drafting genuinely adds value and when it doesn’t, and the instinct to read AI-generated code with scepticism before shipping it
• A passion for making the data orderly

Desirable

• Reporting to: Head of Analytics
• You build the bridge between raw source data and the dbt layer the rest of the team builds on, working across the medallion architecture
• Your craft is modelling raw, messy, real-world source data (Semble, Zendesk, CMS, finance systems) in through the medallion layers, (bronze, silver, gold) and ensuring our analysts get data they can use
• Our team already includes a senior data engineer (focussed on ingestion and transformation) and a senior analytics engineer (focussed on dbt access control and restructuring the data into analyst-ready schemas), you will deputise for them both to increase capacity and reduce key-person risk
• You’ll stay close enough to our analysts that you can tell when a schema decision is going to cause pain three weeks later
• The chair builds the conduit between raw data and analyst-ready data and ensures that conduit is maintainable and governable
• Model bronze source data into silver tables for analytics consumption. The headline first project is Semble, which currently lands in a heavily normalised form that’s not ready to be analysed. An old process (Power BI bolted onto complicated exports) provides an illustration of the business logic to be applied in the restructure, but you’ll have the agency to throw that old process away and build something better
• Build upon our cross-system reconciliations for entities that span our stack — prescriptions across Semble, CMS, pharmacy and EMIS; patient identities fuzzy-joined across CMS, NHS, and insurer sources. Data needs to be reconfigured from “where is it from” to “what is it about”; so our analysts can answer business questions
• Replace a VBA-stitched partner-reporting PowerPoint pipeline with a code-first generator
• Build automated data-quality alerting on referrals and key data feeds. Slack-routable, with a clear ownership trail
• Author new dbt models in your own schema, in separate MRs, reviewed by the Senior Analytics Engineer
• Coach the analysts on engineering skill and discipline — when to use a CTE, when not to, how to use the data platform. We need our analysts to understand engineering and our engineers to understand analytics, so peer teaching and support is a team practice
• What good looks like at 90 days
• Semble silver model in production, with at least one downstream dashboard repointed to it
• One cross-system reconciliation (prescriptions or person-key) shipped, with a documented match-rate baseline
• Our contract-reporting pipeline either replaced or with a credible plan to replace it
• You’ve reviewed a non-CS analyst’s SQL at least once in writing, and they’ve shipped the result

• 1. Short call (30 min) Two-way interview; we don’t want to waste your time. We’ll describe the role so you can determine your own interest. There will be technical questions, focussed on d
• 2. Online Programming Test (15-60 minutes). Focussed on PySpark and DBT. No hard algorithms – we just need to know you find easy programming questions easy (without an AI)
• 3. 2 Part Final Interview (60min/30min) – Technical conversation with the team, followed by a values conversation with the Chief Data Officer. Your choice whether these are back to back or on separate days – whatever is best for you