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…