Data Engineer – AI Product

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Qonto

WebsiteLinkedInAll Job Openings

501 - 1000 employees

💰 $500k Grant on 2022-10

Qonto is the leading European business finance solution. It simplifies everything from everyday banking and financing, to bookkeeping and spend management. Qonto energizes SMEs and freelancers so that they can achieve more.

📋 Description

• 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.

🎯 Requirements

• 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.

🏖️ Benefits

• 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.