Data Engineer for AI Product

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.

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 for use cases such as fraud detection, credit scoring, and accounting automation.
• 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.
• Set the data engineering standard: Establish reusable Python and pipeline patterns the team builds on, creating foundations rather than one‑off solutions.

What We're Looking For

• ML infrastructure experience: Built pipelines and infrastructure that directly support machine learning workflows, understanding feature stores, model registries, and serving layers.
• Python at scale: Fluent in Python for data engineering with solid experience with Spark, dbt, Airflow, or Ray.
• ML workflow understanding: Understand the full ML lifecycle—training, validation, deployment, monitoring—to build the supporting infrastructure.
• Systems thinking: Design data architectures that balance today’s needs with tomorrow’s scale, treating cost, latency, and reliability as first‑class constraints.
• Production mindset: Operated data systems in production and knows how to prevent failures.

What We Can Offer You

• Direct impact at scale: Your pipelines feed models that process transactions for SMEs and freelancers across Europe.
• A rare team configuration: 3 Data Engineers working alongside 15 ML Engineers, ensuring your infrastructure work is immediately stress‑tested.
• Build, don't inherit: Define how Qonto's ML infrastructure is built, with real ownership over architectural decisions.
• Fast iteration cycle: Continuous delivery allows infrastructure improvements to ship frequently and show impact quickly.
• Cross‑functional exposure: Work at the intersection of data engineering, ML, and product, contributing to financial solutions across multiple countries.

Your future manager

Option A

• Marianne Borzic Ducournau, Head of Data Products – graduate of École Polytechnique, former leader of Data Science teams at Uber and Amazon.
• Brings a combination of applied ML expertise and business context from finance, helping people see both technical and strategic sides of their work.

Option B

• Benjamin Wolter, Head of AI Products – earned a PhD in Physics and led ML Engineering and Data Science teams across logistics and digital marketing.
• Brings deep technical ML expertise, practical experience building scalable ML systems, and a management style focused on ownership and autonomy.

At Qonto, we understand that true diversity isn’t just about checking boxes. Apply regardless of the boxes you tick; we welcome people who bring unique perspectives.