> Markdown version of [/jobs/ext/4677014-head-of-data](https://www.wearedevelopers.com/jobs/ext/4677014-head-of-data). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Head of Data - **Company:** Conveo - **Location:** Antwerpen, Belgium - **Experience:** Expert - **Salary:** €115,000 - €130,000 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, BigQuery, Information Engineering, Data Infrastructure, Data Warehousing, Data Streaming, Business Intelligence Development Studio, Data Layers, Google Bigquery - **Published:** November 12, 2026 - **Apply:** https://startup.jobs/head-of-data-antwerp-conveoai-10742280 ## About the Role * Experience leading data at a startup or scale-up, as a Head of Data, Head of Analytics or similar. Ideally you've been at a Series B company or later and have seen what good structure looks like at the next stage * Hands-on BI and analytics experience. You've done the insights work, not only data engineering, and you know what good BI looks like * Enough technical depth to ask engineers the hard questions, and a clear understanding of how data flows from the warehouse through transformation and semantic layers into a BI tool. * A problem solver's instinct: you can talk to a department lead, draw out what they really need, and turn it into analysis * A commercial mindset: you understand how a business works and what it needs from its data * Strong stakeholder skills. Lots of people will want your time, and you prioritise well while keeping their trust * Everyday use of AI in how you work, and a view on how to build data that AI tools can rely on * Experience of the challenges that come with scaling fast, and a clear sense of the structure you'll bring as we grow Nice to Have * Hands-on experience with BigQuery, dbt, Cube or Metabase ## Description You'll be our first Head of Data. You'll own internal data at Conveo and build the team that serves the whole company with it. You won't be starting from a blank page. Our GTM engineering team has built a data warehouse and semantic layer and has made a start on BI, and colleagues in operations have helped shape our metric definitions. What we don't have is one person who owns all of it, knows what good looks like, and turns it into something every team trusts and uses. You'll report to our CFO and work very closely with our CTO, as well as with the engineering team, operations and go-to-market. This is a hands-on role. You'll be in the data yourself, not writing a strategy and handing it over. You'll start as the first member of the data team, with a remit to hire as we grow. This is a role for the long term, not an audit-and-leave project. It starts with the foundations and grows into a bigger team, with room for machine learning and data science to help us predict more across the business. What You'll Do Own the data stack end to end * Take ownership of our warehouse (Google BigQuery), our transformation and semantic layers (dbt and Cube today) and our BI tooling (Metabase today) * Review what's been built with a critical eye, and get it ready to scale as new tools and data sources come in * Think through access rights, so it's clear who can see customer, HR and financial data once it all lives in one place Build one source of truth the business trusts * Own metric definitions, so every team works from the same numbers * Close the gaps that matter most, starting with areas like marketing attribution and the data our finance team needs for its analysis * Make our data reliable enough that people trust it and act on it Turn data into insight for every team * Sit down with department leads, get to the real question behind a request, and build the analysis that answers it * Act as the translator between engineering and the business: ask engineers the hard questions about what to build, and explain data in plain English to everyone else * Swap today's sprawl of one-off dashboards and AI-generated reports for a clear set of things people actually need to look at Use AI well, without the slop * Shape our data so AI tools can use it reliably * Help the business get real value from AI for analysis, without a flood of low-quality output Build the data team * Hire your first team members, and set out what the BI team should look like as we grow * Lay the groundwork for machine learning and data science later on What Success Looks Like * After one month: you know our data infrastructure properly and have run a full gap analysis covering what we take in, what we understand and what's missing. You also have a plan for what to fix first and a plan for the team, including your first hires. * After three months: you've reviewed the setup in place and own BigQuery, the transformation layer and our BI tooling, and our systems are ready to scale. Teams are already using data better because of work you've delivered. * After six months: every department is running with deep insights and analytics, and our leaders have the confidence and ability to use the data themselves. * Throughout: people trust the numbers, the business has the dashboards it needs rather than a thousand it doesn't, and we know who the data team hires next.