Data Analyst

The business is Europe's leading live entertainment platform, owning over 80 festivals including major rock, electronic, and Gen Z-focused events. With F&B playing a huge part in the overall revenue. Working directly alongside the F&B Strategy Lead, the Data Analyst will help build the evidence base that will shape the company's F&B strategy for the next 5 years. The Data Analyst will work with messy, live event data from multiple systems and help turn it into clear commercial recommendations. • • • This is a 6 months FTC role • • • What You Will Actually Do Hands-on Revenue Analysis • Go beyond top-line revenue. Analyse product mix, per-outlet performance, and site-level variances. • Answer questions like: Why did Bar A outperform Bar B? Was it location, queue times, pricing, product range, or staffing? • Identify the underlying drivers of performance – not just what happened, but why. Working with Large, Messy Datasets • Pull sales, volume, and margin data from Square POS across multiple festivals and venues – often inconsistent, incomplete, or differently formatted. • Clean, structure, and build insight layers on top of imperfect operational data. • Investigate why "all data is not in one plan" and help build a single source of truth in PowerBI. Comparative Operating Model Analysis • Model the financial and operational performance of in-house F&B vs outsourced partners (major contract caterers). • Compare good examples vs poor examples within the company's own network. • Benchmark national team performance across different countries – not just totals, but efficiency, throughput, and margin drivers. • You will own the data appendix behind that recommendation – every chart, every driver analysis, every unit economics assumption. Must-Haves (Non-Negotiable): • Hands-on analysis of revenue streams – you have looked at F&B, product mix, or site-level performance, not just top-line totals. • Evidence of identifying drivers of performance – you can point to a time you figured out why something performed well or poorly, not just reported the number. • Experience working with large / messy datasets – you have built insight layers on top of imperfect operational data. Nice to Have (But Not Essential): • Experience in live events, festivals, stadiums, or high-volume hospitality. • Familiarity with Square POS or similar EPOS systems. • Basic SQL or Python for ad-hoc data pulls.