Lead data engineer
Le poste
Preferred Experience
🔍 Who we're looking for
8-15 years of experience in data engineering or data-intensive software development.
Strong coding experience in Python and SQL, with a plus for Rust or TypeScript.
Expertise building ETL/ELT pipelines and orchestration (Airflow, Dagster, dbt, or equivalent).
Experience with data warehouses and lakes (BigQuery, Snowflake, or equivalent) and streaming tools (Pub/Sub, Kafka).
DataOps know-how: versioning, monitoring, data quality and testing.
Proven track record delivering projects in an agile, collaborative environment.
Genuine desire to mentor and grow more junior profiles over time.
Demonstrated ability to thrive in a demanding, fast-growing environment.
Excellent analytical skills, attention to detail, and strong prioritization.
Fluent English, mandatory (working language with our clients and the labs).
The extras that make the difference
Experience with GCP (or equivalent), Modal or GPU cluster and serverless technologies.
Experience with infrastructure as code (Terraform, CloudFormation).
Strong interest in data anonymization, multi-modality, and RL environments.
Recruitment Process
Why join us
Entrepreneurial adventure, play a key role and live the early days of an ambitious company, right at the front line of the AI revolution.
Backed by Y Combinator, join just as we accelerate.
Founding impact, shape the company and its culture from day one.
Offices in central Paris + 1 to 2 days WFH.
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Perks:
- Alan health insurance (mutuelle)
- Unlimited GPT & Claude tokens
- 50% Navigo covered
Additional Information
- Contract Type: Full-Time
- Location: Paris
- Experience: > 7 years
- Occasional remote authorized
- Salary: between 80000€ and 100000€ / year
Ooak data
Ooak Data construit Alexandria, une bibliothèque de jeux de données représentant des workflows réels d'entreprises. La société acquiert des environnements de données complets, les anonymise et les structure afin qu'ils puissent servir à l'entraînement et à l'évaluation des agents d'intelligence artificielle.
L'objectif est de confronter les agents à la complexité des outils, documents et processus réellement utilisés par les entreprises, au-delà des benchmarks académiques. Ces environnements sont destinés aux laboratoires d'IA qui développent des modèles capables d'accomplir des tâches professionnelles concrètes.
Équipe
5 personnes
Ville
Paris