Our mission and customers
Our platform simplifies banking and finance management for SMEs today, so that they can build their tomorrow. We offer a finance management platform with banking at its core, augmented by financial tools. We are proud to be rated 4.8 on Trustpilot, based on 53,000+ reviews.
Our culture puts customer satisfaction at the core of what we do, as proven by our Net Promoter Score of 75. This level of satisfaction is far above typical traditional banking scores, often ranging from 3 to 12, sometimes even lower.
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: France, Germany, Italy, Spain, Portugal, Austria, Belgium, and the Netherlands. We have been profitable since 2023, and we are just getting started as we want to become the indisputable European leader in SME finance management.
Our beliefs
We hire for skills and potential. With 80+ nationalities, 45% women, and 56% of women in our leadership team, diversity is simply part of who we are.
AI at Qonto
We always choose thinking over routine. That's why AI is already deeply embedded in how we work - not as a trend, but as a way to raise the bar for the entrepreneurs who count on us. That is why we grant our Qontoers unlimited access to the best AI tools on the market - Claude Code, Cursor, Copilot, Dust, and Notion AI.
We want people who experiment without waiting for permission. Who push AI beyond the obvious. Who know when to trust it and, more importantly, when to question it.
Already pushing AI limits? You'll fit right in.
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🔒 Your security matters to us
Recruitment scams are on the rise. Keep in mind, we will never work with third-party platforms or agencies that request payment from candidates.
If you receive a suspicious message claiming to be from Qonto, please report it right away (support@qonto.com)
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⭐ Mission: As a Staff Data Engineer, your mission is to architect and implement scalable data platform tools that support AI product development, enabling our teams to deploy and operate AI models effectively. You will act as the technical bridge between AI Product teams and Platform Engineering, playing a central role in Qonto’s strategy to integrate AI across business areas and improve customer experience for our 400,000+ clients. This position is open to full remote contract.
👩💻🧑💻 As a Staff Data Platform Engineer at Qonto, you will:
Bridge & Translate: Act as the technical bridge between ML product teams and platform engineering, translating ML requirements into scalable platform features.
Design Architecture: Lead the design of cross-functional data architecture, ensuring consistency, scalability, and reliability across reporting, analytics, and ML use cases.
Establish Standards: Partner with engineering leadership to establish platform standards, governance frameworks, and best practices.
Optimize Infrastructure: Manage and maintain the data platform (Snowflake, orchestration) with a focus on reliability, performance, and cost.
Drive Quality: Ensure platform documentation is up-to-date and establish robust data quality KPIs and monitoring within your first 9 months.
🤔 What you can expect:
Strategic Context: You will join a rapidly growing environment where AI is becoming increasingly strategic, requiring robust infrastructure in a regulated financial context.
Methodologies: We balance short-term delivery for AI product teams with long-term platform evolution, designing tools almost from scratch.
Team: You will work in a team of experienced Senior and Staff engineers with high autonomy, influencing a tech organization of 500+ engineers.
Tools: We work with Python, Java, Kubernetes, Snowflake, and modern ML platform tools (e.g., MLflow, Kubeflow, SageMaker).
Impact: You will directly contribute to raising the overall AI maturity of Qonto, enabling us to serve our customers better through intelligent automation.
🤝 About your future manager: Your Future Manager will be Charles, Lead of the Data Platform team.
His background? Charles is transitioning from an Individual Contributor role into a leadership position, after building strong expertise in data platform topics and architecture.
What does he bring to the team? Charles has a hands-on, “architect-embedded” leadership style: he stays close to the technical reality, helps unblock complex topics, and supports the team in making pragmatic architectural decisions. He values humility, collaboration, and clear communication, and fosters an environment where autonomy is encouraged and supported through impactful projects.
🏅 About You:
Experience: Proven Data Engineering background with clear progression toward architecture responsibilities, including deep expertise in designing and scaling data platforms. Prior experience as a Data Scientist or Machine Learning Engineer is a plus.
ML Lifecycle Mastery: Strong understanding of the full ML lifecycle: experimentation, training, deployment, monitoring, and retraining, with experience using modern ML platforms and tools (MLflow, Kubeflow, SageMaker, TensorBoard, or similar).
Architectural Thinking: Proven ability to bridge technical and product perspectives, translating business needs into technical solutions, with a track record of making complex trade-offs and driving technical consensus.
Technical Leadership: Track record of influencing technical direction and mentoring engineering teams, with excellent communication skills to articulate technical vision to diverse stakeholders.
Regulated Environment: Experience working in regulated environments (financial services preferred) is a plus, with a strong mindset for governance and compliance.
At Qonto we understand that true diversity isn't just about ticking boxes on a hiring checklist. Apply regardless of the boxes you tick! Who knows? You may have the missing piece of the puzzle we've been searching for all along.
Our hiring process
- Interviews with your Talent Acquisition Manager and future managers (1 hour each)
- A remote or live exercise to demonstrate your skills and give you a taste of what working at Qonto could be like
On average, our process lasts 20 working days - more information here on our candidate journey.
To know how your personal data will be processed during your application process or to request its deletion, please click here. 
