Data Analyst

The business is Europes leading live entertainment platform, owning over 80 festivals including major rock, electronic, and Gen Z:focused events. With F and B playing a huge part in the overall revenue. Working directly alongside the F and B Strategy Lead, the F and B Data Analyst will help build the evidence base that will shape the companys F and B strategy for the next 5 years.

This is not a standard FP and A role. The Data F and 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 and B vs outsourced partners (major contract caterers). Compare good examples vs poor examples within the companys 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 and 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. #J-18808-Ljbffr