At Recare, we are building a multi-product hospital platform, and the data function needs to grow with it.
Today, the team consists of a senior product analyst, a BI analyst who is still being onboarded, and a collection of Looker dashboards at various stages of usefulness. As the company scales, we need a much stronger foundation for understanding what is working, where we are winning, and how product performance connects to commercial outcomes.
As Head of Data (m/w/d), you will lead the data function at Recare. You will define the company’s data strategy, strengthen how success is measured across product teams and the broader business, and build the team and systems needed to make data more useful for decisions.
This is a leadership role, but not a hands-off one. You will join a small team, assess the current setup, work directly with the existing analysts, and help shape the next stage of the function. Over time, the role will shift further towards team building, structure, and strategic direction as the organisation grows.
You will sit in Product, report to the Director of Product, and work in a hub-and-spoke model with a central data team serving teams across the business.
You will help Recare answer a fundamental question: are we winning?
That means creating a clearer picture of whether we are winning customers, building winning products, and growing in the right markets. Today, parts of that picture exist, but they are incomplete and disconnected. You will help make it clear, connected, and actionable.
Purposeful work – your work has a direct impact on patients, hospital teams, and digital healthcare.
Remote-first and flexible work – Recare works remote-first, with flexible working hours and Berlin office access.
Workations and flexibility – depending on setup and agreement, Recare supports flexible work arrangements and time working abroad.
Learning & development – regular feedback, development support, and a learning budget.
Tools and hardware – a modern setup and AI as part of everyday work.
Additional benefits – including an Edenred card and an extra day off on your birthday.
You will define where Recare’s data function is today, where it needs to be, and how to get there.
That includes setting clear KPIs and north star metrics for product teams and product groups, building a strong point of view on what to measure and what to stop measuring, and making sure data supports decision-making rather than becoming decoration.
You will help connect product usage data to business performance.
Today, metrics such as ARR, NRR, adoption, retention, and expansion live in different systems, and the links between them are not always clear. You will help build the connective tissue between product performance and commercial outcomes.
You will help turn data into something the organisation can act on.
That includes quarterly business reviews, product reviews, and other decision-making moments where teams need a clear view of whether strategy is working, whether bets are paying off, and what the numbers are actually saying.
You will lead a team that today consists of a senior product analyst and a BI analyst.
You will assess the current setup, work hands-on with the team from day one, and help grow the function over time. The expectation is that by month 6, you will have a hiring plan in motion, and by month 12, the team should be on a path towards serving the broader business with a team of 5–6.
You will help shape how data supports teams across product, finance, RevOps, and marketing.
This includes deciding how analysts should be allocated and embedded across the business within the hub-and-spoke model, rather than thinking of data as serving product alone.
You will bring a point of view on the right tooling for a company at this stage.
Recare uses Looker today, but not yet in a way that fully supports scale. Datadog is used on the engineering side. You will help define the right stack going forward and lead a migration if needed. This role requires judgment about when product analytics platforms are more useful than general BI tools, and vice versa.
You will be a close partner to the AI engineering team, while not owning AI engineering or the ML pipeline.
There is an important grey zone between product analytics and AI quality measurement. For example, measuring whether products like Voice Desktop are actually saving clinicians time sits partly in product analytics and partly in AI output quality. You will need to understand evaluation, quality measurement, and how to think about non-deterministic AI outputs in partnership with the AI engineering team.
8–10 years in data roles, including at least 3 years leading teams
Progression from analyst or data scientist into data leadership
A strong product analytics background
Experience instrumenting products, defining meaningful metrics, running experiments, and connecting usage patterns to outcomes
Experience doing this across multiple products, not just one
Comfort with commercial metrics such as ARR, NRR, churn, and expansion, and how product data connects to business growth
Experience building and growing a data team from a small starting point into a stronger data organisation
Judgment about what to hire first, how to structure analysts across teams, and when to centralise versus embed
Strong mentoring, coaching, and development skills for junior and mid-level analysts
Clear opinions on tooling, including the trade-offs between tools such as Looker, Amplitude, Mixpanel, and Metabase
Startup or scale-up experience, especially in a company going through the 10–50M ARR transition
The ability to move fast and make practical decisions without overbuilding
Comfort working in regulated industries such as healthcare, fintech, insurance, or similar is a plus
AI literacy, including use of AI tools in day-to-day work and familiarity with LLM evaluation, quality scoring, and how AI products generate and transform data
The ability to be a credible partner to the AI engineering team without being responsible for building models
English as a working language; German is a nice to have, not a requirement
Someone who combines strategy and execution
Someone who can build a function from a small starting point rather than needing a large team from day one
Someone who can connect product performance and commercial outcomes
Someone who can tell clear stories with data, not just produce dashboards
Someone who is opinionated enough to improve the current setup, but practical enough to do it in stages
Someone who is comfortable working across product, finance, RevOps, and marketing
Someone who finds the overlap between product analytics and AI quality measurement interesting rather than uncomfortable
Someone whose entire background is in data engineering and pipeline work
A BI consultant who mainly builds dashboards
Someone who needs a large team before they can be effective
A pure academic or research profile
Recare has a clearer and more useful data strategy
Product teams have better-defined metrics and a stronger understanding of whether their work is paying off
Product usage data is more clearly connected to revenue, retention, and expansion
The organisation has stronger, more actionable narratives in business reviews and product reviews
The data team is growing in a deliberate way, with a clearer model for serving the broader business
Recare has a stronger approach to measuring AI-powered product quality in partnership with the AI engineering team
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