Apprentice - Data governance Engineer (x/f/m)

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