Head of Data (m/f/d)

WHY THIS ROLE EXISTS

Anyone can stand up a warehouse. The hard part is taking a good data foundation and making the whole organisation trust it, use it, and decide with it. That's where Numa is. Having grown to 40 cities across Europe in just six years, we already have a solid data stack and most of our data centralised in the warehouse. The opportunity now is to take it to the next level: from data that's available to data that's proven, accurate, and trusted enough to drive commercial decisions — and to bring the remaining pockets that still live in spreadsheets into the same foundation.

The order matters: good process creates trusted data, trusted data creates KPIs leaders can decide on, and only then does AI add real leverage rather than noise. We're already experimenting with AI analytics; this role makes that robust — better practices, higher trust, and data that is genuinely AI-ready, so the experiments become a dependable capability.

Just as important is the human side: raising data literacy across the company and enabling department heads to own and self-serve their own numbers, so good data drives everyday decisions rather than sitting in a central team's queue. This role owns that shift — not as an engineer who builds pipelines or an analyst who pulls numbers, but as the person who sets the data strategy, translates commercial needs into a scalable architecture, and lifts the whole organisation's data maturity.

WHAT YOU'LL OWN

You sit at the intersection of three things, and the rare skill is connecting them:

• Commercial translation. This is the core of the job. You partner with Finance, Real Estate, HR, Growth to understand what they actually need, then translate that into the right data strategy — scalable and sustainable, not a one-off.
  
• Quality, trust, and AI-readiness. You take our data from available to dependable — proven, accurate, and high-quality enough that leaders decide on it without a second source. That includes bringing the remaining data still living in spreadsheets into the shared foundation, and raising the bar on governance and quality across the board. It's as much about people as architecture: making the shared, trusted version the obvious choice, and lifting data literacy so teams use it well. This is also what makes our AI analytics robust rather than experimental — trustworthy, well-modeled data is the precondition for AI that can be relied on, and you make that foundation real.
  
• Strategy & architecture direction. You set the direction for the data stack — what each layer is for, what we should test, what we adopt or retire — and you bring rigour and challenge to those choices. You don't own or run the platform; our Data Engineering lead owns execution and does it well, with strong opinions of his own and the standing to challenge you back. Your job is the strategic counterpart to that: pushing new solutions, pressure-testing the architecture, and making sure every layer earns its place — so the stack is challenged by more than one person. You need a clear, opinionated grasp of the options and the judgment to make build/buy/test calls without being attached to any one technology.

WHAT WE'RE LOOKING FOR

• Senior judgment on the modern data stack — you can explain what a semantic layer is, what a warehouse is, what the credible options are, and defend a stack choice (and argue the opposite). Depth in any single tool is welcome but not the bar.
• A track record of commercial translation — you've sat with non-technical business teams, understood their domain well enough to model it, and turned that into durable data structures. This is the disqualifying gate: strong elsewhere but weak here is a no.
• A track record of raising data maturity — taking a working data setup to the next level on quality, governance, and trust, and consolidating the last pockets of siloed data into a shared foundation. Bonus if you've won over the people who were attached to their own spreadsheets.
• An AI-ready mindset — you treat AI fluency as the payoff of clean, governed data, and know how to make AI analytics trustworthy and robust rather than experimental.
• Comfortable as a peer to senior commercial leaders; credible with both a GM and a C-level audience.