Data Engineering & Analytics Manager

Insurance is a numbers business, and most of those numbers are harder to trust than they look. You would lead the team that defines the numbers Insify runs on, from the event a customer fires on our website to the report a risk carrier audits months later.

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About the Role

You would lead our Data & Analytics team of 3-4 engineers end to end: ingestion, the warehouse and its models, the metric layer, and the analysis and reporting built on top of it. It is a small team with a big surface, so there is nowhere to hide and nothing you can't reach.

We are deliberately not splitting data engineering from analytics. At our size the hard problems sit in the handover between the two, and we want one person who enjoys that whole puzzle.

The team serves four groups with genuinely different needs. Growth wants hourly to daily signals and can live with approximately right. Carrier reporting and corporate finance are calendar-locked and need numbers that reconcile and survive an audit months later. Actuarial needs depth and lineage over speed. Prioritising here is arbitration between things that fail in different ways, and the arbitration is yours.

It's a hands-on role. You can open the warehouse and read the models, you review a new data model before it ships, and your team trusts your technical judgement because you still use it. The job is still the team: its own game, its communication, and each person in it. When something slips, hands-on work is the first thing that yields.

What you'll be doing

Leading a team of 3-4 data engineers and analysts end to end: what it builds, how it runs in production, and who you hire and grow to get there

Running discovery with the teams we serve: understanding what they need from data, and bringing the platform and datasets that enable them

Owning the trade-offs between Growth, carrier reporting, finance and actuarial: deciding what the team works on, driving alignment with key partners across other teams, and saying no when the answer is no

Making the platform scale: a new country, carrier or product should mean configuration and reused models, and recurring manual work, like carrier reporting and cash reconciliation, should turn into automation

Bringing engineering excellence to how the team works: data contracts with product teams, testing in CI, alerts that get resolved, a way to try changes before they hit production

Challenging our ideas and bringing your own. We encourage each other to act this way, so you can expect the same from others

What you'll need

You've managed or led a data team before, including hiring, feedback and growing people. It doesn't have to be for long, but it has to be real and you have to want more of it

Depth on one side of the data stack and working fluency on the other. You still love shipping, you review your team's work, and they trust your judgement on it

A track record of shaping what gets built, not only how: you've turned business problems into data priorities, cut scope, and can show the decisions that followed

Ownership & impact instinct - when needed you go beyond your team's boundaries to get to results

You get real leverage out of AI in your daily work, and you can say where it helped, and where it hurt. Your team gets that from you: you try new tools and ways of working first, and bring the ones that stick

Nice to have

Experience with reporting that has to reconcile: bordereaux, revenue, cash, ledger

Experience building B2B products, especially in insurance & fintech

Experience working on a product distributed across different country markets

Experience with Databricks, dbt and event tracking (Snowplow or similar)

Our stack

Databricks with Unity Catalog, dbt with SQL-based transformation, running on AWS.

None of this is a hard requirement, and you don't need to know all of our stack. We care more about how you reason about data, systems and teams than which tools you have used. Strong on most of it? Apply.

How we hire

Five conversations, each with a clear purpose, and you hear where you stand after every one:

Leadership. We look at your experience with performance management, personal development and building teams.

Data Model Review. You review a change to our warehouse models with two of our engineers.

Data Platform Design. A modelling and platform problem close to what we deal with.

Values Interview. We look at your approach to work, and how it adds to our Values.

Meet the Founder.

You get the outcome within three days of your last round, with specific feedback either way.

We offer

Competitive compensation. We offer a salary in the range of 85k-110k EUR plus performance-based equity grants

Hybrid. 3 days in the office, 2 from home, up to 4 weeks a year from abroad, 25 holiday days

Pension. 6% employer contribution

Wellbeing budget. €50 net a month via Epassi, from the gym to takeaway, rolls over

Learning and coaching. Internal leadership programs and coaching

Central Amsterdam office. Shared lunches, dogs, Friday drinks, high-spec MacBook Pro

About Insify

Insify is a high-growth scale-up backed by €43M in total funding from world-class investors like Accel, Munich Re Ventures, and Formula 1 champion Nico Rosberg. In five years, we've expanded from the Netherlands into France and Germany, and we're just getting started. Our mission: become the European leader in AI-powered SME insurance.

We aren't just building a company; we're leading a revolution. Our culture is intense, high-stakes, and mission-driven. We hire smart, ambitious "owners" who possess a relentless drive to win. This is an environment for those who thrive on challenges, value intellectual honesty, and share our mindset of becoming a global category leader.

Our founding team consists of seasoned entrepreneurs who have previously built iconic businesses like HelloFresh, Bloomon, and SumUp. We bring that same bias for speed, scale, and high standards to one of the most structurally broken experiences in financial services. Together, we combine that deep fintech and e-commerce expertise with cutting-edge AI to stay ahead of the curve.

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