VP of Data

The company

Our client is a product and technology company built around data, mathematics, and modelling. The core of the product is solving optimisation and prediction problems that most of the industry considers impossible; that’s where their models and their data do the heavy lifting. They operate across several European markets, are founder-led, and are backed by long-term venture and growth investors. After a few years spent building operational and financial strength, the company is putting growth back at the top of the agenda, and data is a key part of how it gets there.

It’s an engineering- and analytics-led place: high standards, low ego, and people who like hard problems for their own sake.

The role

The VP of Data owns the whole data organisation: platform, engineering, and analytics. It reports into the company’s technical leadership, which puts you close to the founders and to where the company is run, with a clear mandate to shape one of its most important functions.

The job is to build something that lasts: a data landscape and a data culture where ownership sits across the whole organisation, well beyond the tech team, and where data feeds decisions, product, and AI alike. You’d inherit a senior team, with leads already in place for platform/engineering and analytics, so the foundation is there to build on.

Who you are

• You’ve built and run a data organisation before and shaped its culture, not just stepped into one that already worked.
• You think long-term about the data landscape itself, the thing that keeps paying off for analytics, AI, and product, instead of a queue of one-off deliverables.
• You’re technical enough to understand and challenge both data engineering and analytics, without needing to be the deepest one in the code.
• You can push change through an organisation, shifting how people work with data and bringing them along, and you’re a clear communicator with leadership and the business side.
• You bring the analytical sharpness and structure you’d associate with management consulting, but your hands-on experience is in building data inside modern product or tech companies.
• Bonus if you see data and AI as the same discipline, and can carry the data foundation into AI instead of treating it as a separate track.