Job Description
We are looking for a Data Engineer - Data Governance (apprentice) to join the Data Governance team. Data Governance at Doctolib ensures company-wide data is reliable, well-structured, and accessible, while enabling advanced analytics and AI through strong governance foundations embedded in the data platform.
At Doctolib, we leverage innovation to improve the daily lives of more than 900,000 professional users and serve 90+ million patients across Europe. As we build the future of healthcare AI, ensuring data is trusted, secure, and scalable is critical this is where this role plays a key part.
This Data Engineer - Data Governance role sits within the technical stream of the Data Governance team. Its mission is to implement and operationalize governance frameworks across the entire data lifecycle from raw data ingestion to analytics consumption.
You will work on structuring, standardizing, and governing data across multiple layers of the data platform, ensuring governance is not only defined but effectively embedded into tools, pipelines, and workflows.
What you’ll do
As a Data Engineer - Data Governance apprentice, you will contribute to:
Implement data governance across the data platform
- Apply governance frameworks across all layers of the data lifecycle (raw data, transformed data, analytics)
- Ensure governance practices are embedded directly into pipelines and tooling
Build and maintain data taxonomy
- Categorize and classify data assets (events, tables, datasets, files..) across the platform
- Ensure data is clearly defined, tagged, and aligned with business domains
- Enable scalable governance (access control, ownership, compliance) through proper classification
Contribute to the Data Catalog
- Improve data documentation, metadata, and discoverability
- Ensure datasets are properly described, owned, and trustworthy
- Integrate catalog usage into the data ecosystem and workflows
Leverage AI to scale governance
- Use AI tools (e.g. Claude or similar LLMs) to automate documentation, tagging, and data quality processes
- Experiment with AI agents and automation workflows to industrialize governance practices
Act as a bridge across teams
- Collaborate with Data Engineers, Analytics Engineers, and business teams
- Translate governance requirements into technical implementations
- Help teams adopt governance best practices in their daily workflows
Who You Are
You could be our next teammate if you:
Are a Master’s Degree student (M2) or Engineering school student looking for a 1- or 2-year apprenticeship
Have strong foundations in data engineering:
- Python ( mandatory)
- Basic understanding of data pipelines and the end-to-end data lifecycle
Are familiar with modern engineering practices:
- Git / GitHub
- Basic CI/CD concepts
- Cloud environments (GCP is a plus)
- Kubernetes is a plus
Are interested in data governance topics:
- Data quality, metadata, data catalog
- Access management and data lifecycle
- Data taxonomy and structuring
Are comfortable working across technical and functional topics
Are able to translate functional needs into technical implementation
Are curious about AI and automation:
- Comfortable using AI tools (LLMs like Claude)
- Interested in building simple automation or AI agents
Why this role is unique
- You work across the entire data value chain, from raw data to business usage
- You combine Data Engineering and Data Governance, a rare and highly impactful skillset
- You implement governance in practice, not just in theory
- You collaborate with all data stakeholders, gaining strong exposure
- You work on foundational topics (taxonomy, catalog, access, data quality) that scale with the company
- You leverage AI to industrialize data governance
The interview process
- Recruiter interview (30 minutes) send use case at the end of interview
- Operational interview with the hiring manager (1 hour) + Tools member
- Final interview with the Head of Data Governance (20 minutes)
- Offer
Job details
- 1- or 2-year apprenticeship
- Start date: July / September 2026
- Location: Levallois-Perret
- Hybrid work model (3 days on-site per week)
- Remuneration: TBD
