Analytics Engineer
Kingston-on-Thames (4 Days in Office, 1 Day from home)
37.5 hours per week
About the business:
Crew Clothing Company is a British lifestyle brand operating across four brands — Crew Clothing, Pringle of Scotland, Saltrock and Ben Sherman — with 120+ stores, a growing digital channel and an omnichannel customer experience. We are in the middle of a significant technology transformation: a new eCommerce platform, a new third-party logistics partner, and an upgrade to our ERP platform, all running concurrently and targeting delivery before the end of 2027.
Our IT team is small and highly capable. We are building an internal integration function and strengthening our in-house Data & Analytics capability — and these four roles are the foundation of that work.
Purpose of the role:
Crew Clothing’s Data & Analytics team has built a modern data platform — a Microsoft Fabric Lakehouse sitting and a growing Power BI semantic model spanning sales, stock, returns and customer data. As Analytics Engineer, you will turn that infrastructure into the analytical products the business actually uses to make decisions.
This is a genuinely AI-first role. You will work daily with Claude Code and the latest Microsoft MCP (Model Context Protocol) tooling to build our data lakes, semantic models, and dashboards at a pace that a traditional tools simply cannot match — this is one of the most cutting-edge roles in UK retail analytics today, and you will help shape what AI-native analytics engineering looks like at Crew.
Your primary focus will be driving forward our integration and dashboard roadmap of data products, semantic models, and natural language AI interfaces that let trading, buying, retail and finance teams make decisions on live data rather than instinct.
Key Responsibilities:
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Own delivery against the Crew D&A Strategic Roadmap — the team’s single source of truth for integration and dashboard priorities
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Build, maintain and document Fabric Lakehouse — owning the Python and SQL transformation logic that turns Datitude’s Silver tables into business-ready datasets
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Extend and maintain the Power BI semantic model — building DAX measures, calculation groups and relationships as new data sources and brands (Ben Sherman, Pringle, Saltrock) come online
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Use Claude Code and the latest Microsoft MCP tooling as a core part of your daily workflow — this is an AI-first role and you will be expected to use cutting-edge AI tooling to build and document faster than a traditional tools allow
Apply basic DevOps practice to analytics engineering — Git-based version control, structured deployment pipelines (Dev- Prod), and clear environment governance
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Own and monitor Fabric Data Pipelines ingesting data from multiple sources — incremental load logic, agent driven failure alerting and data quality checks
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Design and build the complex dashboards senior leaders and trading teams use directly to make decisions — trading performance, stock health, buying analysis, customer behaviour, store performance
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Use Python (Pandas, PySpark) for data manipulation and automation wherever SQL and DAX are not the right tool
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Lead the technical onboarding of Ben Sherman, Pringle and Saltrock data onto the shared Fabric platform, enabling group-level reporting
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Maintain a data dictionary and clear documentation of view logic, naming conventions and the deployment process
Skills & Experience Desired:
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3+ years’ experience in analytics engineering, BI development or data engineering
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Strong SQL — able to write and optimise complex views, CTEs and window functions
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Hands-on Power BI experience, particularly DAX measure development and semantic model design
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Comfortable with Python for data manipulation and automation (Pandas, PySpark)
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Familiarity with basic DevOps practices — Git-based workflows, deployment pipelines and version control
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Experience with Microsoft Fabric, Databricks or a similar cloud data engineering platform
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Genuine enthusiasm for AI-assisted development — comfortable using tools like Claude Code and MCP-based tooling to work faster and think differently about how analytics engineering gets done
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Commercial awareness — able to translate a retail business problem into data requirements without heavy specification
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Experience in retail, fashion or ecommerce data environments is a strong advantage
