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.
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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Join us as a Data Engineer (ML Infrastructure) to build the data layer that powers Qonto's machine learning products. Working alongside 15 ML Engineers, you will own the pipelines, feature stores, and model serving infrastructure that turn raw financial data into production-grade ML — so engineers spend their time on models, not plumbing.
You will report to Marianne Borzic or Benjamin Wolter — see manager section below.
➡️ What you'll do- Build and own ML data pipelines: Design, implement, and maintain Python pipelines that ingest, transform, and deliver datasets for model training and inference — covering use cases like [fraud detection / credit scoring / accounting automation — confirm with HM].
- Own the feature store: Design storage and access patterns for large-scale feature datasets, balancing latency and cost so ML Engineers can retrieve features reliably at both training and serving time.
- Drive model serving infrastructure: Implement and maintain the infrastructure that deploys trained models into production, including versioning, scaling, and rollback.
- Build data quality and drift detection systems: Work with ML Engineers to catch data issues before they degrade model performance in production — making reliability a shared standard, not an afterthought.
- Set the data engineering standard: Establish reusable Python and pipeline patterns the team builds on — creating foundations, not one-off solutions.
- ML infrastructure experience: You've built pipelines and infrastructure that directly supports machine learning workflows — not just ETL. You understand what feature stores, model registries, and serving layers are and why they matter.
- Python at scale: You're fluent in Python for data engineering and have solid experience with [Spark / dbt / Airflow / Ray — confirm stack with HM]. You write code others can maintain.
- ML workflow understanding: You don't build models, but you understand the full ML lifecycle — training, validation, deployment, monitoring — well enough to build the infrastructure that serves each stage.
- Systems thinking: You design data architectures that balance today's needs with tomorrow's scale, treating cost, latency, and reliability as first-class constraints.
- Production mindset: You've operated data systems in production. You know what breaks and how to prevent it.
- Direct impact at scale: Your pipelines feed models that process transactions for SMEs and freelancers across Europe. When you improve data quality or reduce feature latency, it shows up directly in product.
- A rare team configuration: 3 Data Engineers working alongside 15 ML Engineers — a ratio that means your infrastructure work is immediately stress-tested by the people who depend on it most.
- Build, don't inherit: Qonto's ML infrastructure is still being built. You won't be handed a legacy system — you'll define how it's done, with real ownership over architectural decisions.
- Fast iteration cycle: We work with continuous delivery, so infrastructure improvements ship frequently and you see their impact quickly — not in a quarterly release.
- Cross-functional exposure: You'll work at the intersection of data engineering, ML, and product, contributing to financial solutions for SMEs across France, Germany, Italy, Spain, and beyond.
Option A
Your manager will be Marianne Borzic Ducournau, Head of Data Products.
- Her background? A graduate of École Polytechnique, Marianne went on to lead Data Science teams at Uber and Amazon in San Francisco before joining Qonto four years ago to build our Data Science team from scratch — hiring the founding members and defining the technical direction.
- What does she bring to the team? A rare combination of applied ML expertise and business context from Finance — she helps people see both the technical and the strategic side of what they're building.
Option B
Your manager will be Benjamin Wolter, Head of AI Products.
- His background? After earning his PhD in Physics and leading ML Engineering and Data Science teams across last-mile logistics and digital marketing, Benjamin joined Qonto to lead our AI Products team.
- What does he bring to the team? Deep technical ML expertise, practical experience building scalable ML systems, and a management style built around ownership and autonomy — he creates the conditions for people to grow without hand-holding.
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