Data Platform Engineer

Overview

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 Linked In 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.

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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.

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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.

• 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, Sage Maker).
• Impact: You will directly contribute to raising the overall AI maturity of Qonto, enabling us to serve our customers better through intelligent automation.

Your Future Manager will be Charles
, Lead of the Data Platform team. Charles is transitioning from an Individual Contributor role into a leadership position, after building strong expertise in data platform topics and architecture.

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.

• 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,…