Data Engineer – AI Product

This is a fully remote position, open to applicants in France.

📋 Description

• Develop and manage machine learning data pipelines: Create, implement, and sustain Python pipelines that ingest, transform, and deliver datasets for model training and inference — encompassing use cases such as [fraud detection / credit scoring / accounting automation — confirm with HM].

• Oversee the feature store: Design storage and access strategies for large-scale feature datasets, balancing latency and cost to ensure ML Engineers can reliably retrieve features during both training and serving.

• Lead model serving infrastructure: Create and maintain the infrastructure that deploys trained models into production, covering aspects like versioning, scaling, and rollback.

• Establish data quality and drift detection systems: Collaborate with ML Engineers to identify data issues before they impact model performance in production — making reliability a shared objective, not an afterthought.

• Set the standard for data engineering: Develop reusable Python and pipeline patterns for the team to build upon — establishing foundations instead of one-off solutions.

⛳️ Requirements

• Experience with ML infrastructure: You have built pipelines and infrastructure that directly support machine learning workflows — not merely ETL. You are familiar with feature stores, model registries, and serving layers and understand their significance.

• Proficiency in Python at scale: You are skilled in Python for data engineering and possess substantial experience with [Spark / dbt / Airflow / Ray — confirm stack with HM]. You write maintainable code for others.

• Understanding of ML workflows: While you do not build models, you have a solid grasp of the entire ML lifecycle — training, validation, deployment, monitoring — sufficient to construct the infrastructure that supports each stage.

• Systems thinking: You design data architectures that reconcile current requirements with future scalability, treating cost, latency, and reliability as primary considerations.

• Production experience: You have operated data systems in production environments. You understand potential failures and how to mitigate them.

🏝️ Benefits

• Significant impact at scale: Your pipelines support models that process transactions for SMEs and freelancers throughout Europe. Enhancements in data quality or reductions in feature latency are directly reflected in the product.

• Unique team structure: Three Data Engineers collaborating with fifteen ML Engineers — a ratio that ensures your infrastructure work is promptly tested by those who rely on it the most.

• Build from the ground up: Qonto's ML infrastructure is still under development. You won't inherit a legacy system — you'll have the opportunity to shape its direction with genuine ownership over architectural decisions.

• Rapid iteration cycle: We utilize continuous delivery, allowing for frequent shipping of infrastructure improvements and quick visibility of their impact — rather than waiting for quarterly releases.

• Cross-functional collaboration: You will operate at the intersection of data engineering, ML, and product, contributing to financial solutions for SMEs across France, Germany, Italy, Spain, and beyond.