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 F&B Data Analyst will help build the evidence base that will shape the company’s F&B strategy for the next 5 years.
This is not a standard FP&A role. The Data F&B Analyst will work with messy, live event data from multiple systems and help turn it into clear commercial recommendations.
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.
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