Analytics Engineer

Our mission and customers: We are creating the freedom for SMEs to succeed by delivering Europe's leading finance workspace with banking at its core, augmented by financial tools. We are proud to be rated 4.8 on Trustpilot, based on 55,000+ reviews. Our culture puts customer satisfaction at the core of what we do, as proven by our Net Promoter Score of 75 (more about our culture here).

Our journey: Founded in 2017 by Alexandre and Steve, Qonto has grown to 1,600+ Qontoers serving over 600,000+ customers across 8 European countries. We have been profitable since 2023, and we are just getting started.

Our beliefs: We hire for skills and potential. With 80+ nationalities, 45% women, of which 56% of women in our leadership team, diversity isn't a program; It's who we are. We've built a discrimination-free hiring process because the best teams are built on merit.

AI at Qonto: AI is deeply embedded in how we work (here) - Every Qontoer gets unlimited access to the best AI tools. We want people who experiment without waiting for permission, push AI beyond the obvious, know when to trust it, and when to question it.

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➡️ Mission: Join us as Analytics Engineer x Business Analytics and become the person our Business Analytics teams can build on without a second thought.

You will own end-to-end the data models feeding Product, Growth, and Ops Finance analytics — designing scalable dbt models, pushing back on requests that would trade reliability for speed, and helping the team migrate to Omni and a real semantic layer.

You will work closely with Jules Jeanroy, our Analytics Engineering Manager, and partner daily with Business Analysts across Product, Growth, and Ops Finance. The team is at a pivotal moment — investing in scalability, cutting technical debt, and building the standards that will define how Analytics Engineering works at Qonto for years to come.

➡️ As an Analytics Engineer at Qonto, you will

• Build data models Business Analytics teams trust — design and ship dbt models and tests that hold up under real, everyday use across Product, Growth, and Ops Finance

• Partner with Business Analysts, not just execute for them — understand what they actually need, and push back when a request would trade long-term reliability for a quick fix

• Reduce technical debt at scale — help migrate parts of the data stack to a more scalable setup, including our ongoing move to Omni

• Own your projects end-to-end — take work from discovery through delivery, and follow up on its real impact instead of just closing a ticket

• Scale your own workflow with AI — use AI tools to speed up documentation, testing, and modeling work, while keeping the judgment calls in your hands

➡️ What you can expect

• Join at a pivotal moment — the team is scaling and migrating to Omni; you're helping shape what comes next, not just maintaining what already exists

• Room to move — grow horizontally across Business Analytics, Compliance, and Foundation scopes, or work toward mentoring and standards ownership within the BA team as a senior profile

• Business partnering is core, not incidental — you'll work daily with Product, Growth, and Ops Finance stakeholders; challenging a request without damaging the relationship matters as much as your technical chops

• AI is the multiplier — we use AI daily at Qonto (read our vision); we expect you to use it too, not just as a coding assistant but to speed up modeling, documentation, and testing

➡️ About your future manager: Your manager will be Jules Jeanroy, our Analytics Engineering Manager.

Jules joined Qonto in March 2026 to lead the Analytics Engineering team that partners with Business Analysts. He focuses on strengthening the way Analytics Engineers and Business Analysts work together, building reliable, high-quality data foundations, and scaling data practices as Qonto grows. Before Qonto, he worked as Lead Analytics Engineer at Spendesk and as a BI Engineer at Brevo, back in the pre-dbt world. His leadership style is direct and collaborative. You can hear him talk about Analytics Engineering in French in this podcast.

➡️ About You

• Analytics Engineering fundamentals — you're strong in dbt and SQL, especially dimensional modeling; you've shipped production-grade models other teams build dashboards on top of

• Comfortable pushing back — you know how to challenge a stakeholder request that would hurt data quality, without shutting down the relationship

• Built for scale, not just speed — you design models that hold up months later, and know when "good enough now" beats "perfect later"

• AI-native work style — you use AI tools beyond just chatting, to speed up modeling, documentation, or test generation

• Appetite for ownership — you're ready to take projects end-to-end, and, if senior, to help raise the team's standards too