As a Staff Data Engineer at Qonto, you'll design scalable data architecture, ensure data quality, and bridge ML teams with platform engineering to support AI product development.
Our mission? Creating the freedom for SMEs to succeed in business and beyond, by delivering Europe’s leading finance workspace. We combine business-class tools (seamless invoicing, spend management, and pre-accounting) with unwaveringly attentive 24/7 support, designed to help businesses breeze through all things finance.
Our journey: Founded by Alexandre and Steve in July 2017, Qonto has rapidly gained trust, serving over 600,000 customers. Thanks to our wonderful team of 1,600+ Qontoers, we also made it to the LinkedIn Top Companies French ranking!
Our values:
Customer focus | Prioritize customers in everything you do
Ownership | Own your part, get things done
Teamwork | Make (team)work easy
Mastery | Continuously raise the bar
Integrity | Always do what’s right, and respect people
Our beliefs: At Qonto, we're committed to fostering a welcoming environment where everyone can thrive. We prioritize evaluating applicants based solely on skills and potential, ensuring diversity with 55% international team members, 44% women, and 20% parents. Join us in building a workplace that celebrates diversity and individuality.
Discover the steps we took to create a discrimination-free hiring process.
⭐ Mission: As a Staff Data Engineer - MLOps, 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 - MLOps 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: 8+ years of experience in data engineering with demonstrated progression toward architectural roles, including deep expertise in designing and scaling data platforms.
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.
🎁 Perks
A tailor-made and dynamic career track. An inclusive work environment. And so much more to help you succeed.
- Offices in Paris, Berlin, Milan, Barcelona, and Belgrade;
- Competitive salary package;
- Meal vouchers;
- Public transportation reimbursement (part or global);
- A great health insurance (depending on the country);
- Employee well-being initiatives: access to Moka Care to take care of your mental health and great offers for sports and wellness activities;
- A progressive disability and parenthood policy (1 in 6 of Qonto employees is a parent!) and childcare benefits with selected partners;
- Monthly team events.
💬 Our hiring process:
- Interviews with your Talent Acquisition Manager and future managers
- A remote or live exercise to demonstrate your skills and give you a taste of what working at Qonto could be like
Find more information about our interview process on our careers website.
On average, our process lasts 20 working days and offers usually follow within 48 hours 🤞
To learn more about us:
Qonto's Blog | Les Échos I L'Usine Digitale | Courrier Cadres
To know how your personal data will be processed during your application process or to request its deletion, please click here.
Top Skills
Java
Kubeflow
Kubernetes
Mlflow
Python
Sagemaker
Snowflake
Tensorboard
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