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# Data Analyst

- **Company:** Wuffes
- **Location:** London, UK (Remote available)
- **Experience:** Experienced
- **Contract:** Temporary to permanent
- **Skills:** Artificial Intelligence, BigQuery, Code Review, Software Debugging, Python (Programming Language), Standard Sql
- **Published:** September 18, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=c6d62325bf6ddbca

## About the Role

* 3-5 years in analytics in a D2C, eCommerce or high-growth environment.
 * Commercial fluency - comfortable with contribution margin, CAC and payback, cohort retention and lifetime value, and able to hold your own with a CFO or growth lead on what a number means for the P&L.
 * Strong SQL and experience with a modern cloud warehouse (BigQuery preferred) and a layered modelling tool (Dataform or dbt).
 * Genuinely AI-native: you already work with AI coding and analysis tools daily, can write a specification an agent can follow, and review its output sceptically rather than shipping it (we use Claude and Claude Code currently but are happy to experiment).
 * Able to review code you didn't write and spot a logic or data quality error before it reaches a dashboard; comfortable in Git/GitHub day to day and with enough Python to read, debug and extend existing scripts.
 * Experimentation literacy - you know what makes a result trustworthy and have the confidence to say a test can't be read.
 * Curiosity and pragmatism: fast to a directionally correct answer, with the judgement to know when exact is required, and excellent written communication for a remote, senior audience.

## Description

* Answer the commercial questions the business is deciding on - acquisition efficiency, retention and lifetime value, pricing, promotions, product launches, channel mix - starting from the recommendation, not the chart.
 * Publish a substantial piece of analysis every month, naming the decision it informs and the value at stake.
 * Act as the embedded analyst for a part of the business (most likely Growth and Retention, plus marketplaces), close enough to know what's coming before it lands as a request.
 * Design and read measurement: hypotheses, control groups, duration and decision rules agreed before launch, with results logged back to the warehouse rather than left in a vendor tool.
 * Build and maintain the dashboards the business runs on, from specification through to production, and model data in BigQuery using Dataform.
 * Own our core metric definitions and keep them consistent - one definition, one implementation, one owner - and reconcile to source systems to catch issues before stakeholders do.
 * Review AI-generated code, your own and others', before it ships.
 * Enable self-serve across the business: warehouse and AI access, metric and dashboard catalogues, training.