Entrepreneur in Residence Data (m/f/d)

Who your work is for

• Corporate customers in vclOS: Better search, recommendations and weekly company updates that make our core product stickier.
• API and MCP partners: A data product others build on – and pay for.
• AI agents & search: Data with provenance, confidence and freshness that makes our answers better than anyone else's.
• Marketing and sales: Directory, rankings and reports through a quarterly data release that bring in pipeline.

When these conflict, the product wins. What you own

• The data business case: which data we sell, to whom, at what price, and how it ties into our core product.
• The data roadmap , ranked by customer value, revenue potential and cost. You set priorities; engineers build and run at scale.
• Data products and packaging: API/MCP access, reports, rankings and premium datapoints – from offer to pricing.
• The data budget and partnerships: vendors, APIs, scraping partners and scouting effort. You test sources on real samples, negotiate, and build partnerships where buying beats building.
• Go-to-market with sales and marketing: first pilots, quarterly data release.
• Data quality as a selling point: coverage, accuracy and freshness we can show to customers.

You work closely with our CEO, join product planning, and prototype datapoints yourself before handing over what needs to scale. Your first 90 days

By day 90: we know which data customers will pay for, the first data offer is live with pilot customers or partners, relationship data is classified and measured, and there is a business plan for the data business for next year.

Month 1: baseline and market

• Analyse usage and demand: which data customers use in vclOS (PostHog, search queries) and what sales and customer success get asked for.
• Map our data assets: top 25 datapoints with source, quality, cost and customer value.
• Competitive landscape: PitchBook, Crunchbase, Dealroom & co. – where we are better, cheaper or unique.
• Next year's data budget as a business case with a cost per datapoint.

Month 2: first offer – corporate–technology relationships

• Define our strongest asset: corporate–technology relationships (mention, partnership, PoC, implementation, investment, acquisition), classified and measured.
• Package it into a first offer (API/MCP access, report or premium module) with pricing.
• Negotiate with 1–2 data vendors or partners where buying beats building.
• Plan the first quarterly data release with marketing.

Month 3: pilots and business plan

• Run 2–3 pilots with paying or committed customers or partners.
• Ship 3–5 new datapoints customers asked for; publish a kill list for the rest.
• Business plan for the data business: revenue targets, pricing, team and budget for next year.
• Start a monthly data review with leadership, product and sales.

Who you are

An entrepreneur with a data background – you've founded something, or built a data product from zero to revenue inside a company. Business-first, and technical enough to prototype alone. You'd rather sell a rough offer this month than a perfect one next year.

Must-haves

• 5+ years with company, market or B2B data in a product, business or founder role, ideally at a data business, marketplace or SaaS company.
• You've built a product or business line from zero and owned revenue, budget or P&L.
• Commercial experience: you've packaged and priced a product and closed first deals.
• Negotiation with data vendors, partners or customers.
• Technical enough to prototype: Python and SQL for quick analyses and scrapers; hands-on with LLMs.
• Working knowledge of GDPR for company and people data.
• You explain data trade-offs clearly to customers, colleagues and leadership.
• Based in Berlin, or willing to relocate.

Nice to have

• Founder or early-employee experience.
• Buying or selling data from providers such as PitchBook, Crunchbase, Dealroom or company registers.
• Experience with API or data-as-a-service pricing and packaging.
• Knowledge of the corporate innovation, VC or emerging technology ecosystem.
• German at reading level: DACH registers and customers are a big part of our upside.

Stack: Postgres, Hasura, Python, PostHog and LLM APIs.

What this role is not:

• Not an advisory role: you build, sell and ship, you don't write strategy decks.
• Not BI or analytics: no dashboards for their own sake.
• Not a data platform architect: engineers own infrastructure.
• Not ML research: you use models, you don't train them.
• Salary band: €80 – €150k base
• Founder-level ownership of a new business line, with a budget and a direct line to our CEO.
• Room to grow: if the data business takes off, you build and lead the team around it.
• Fast impact: your data shows up for corporate customers, partners and in agent answers within weeks.
• A small, senior team. Full-time, Berlin office.

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Source: Statistisches Bundesamt (Destatis), table; dl-de/by-2-0; StarIn's calculation