About

Since 2017, I’ve been building data systems in contexts where reliability is non-negotiable: healthcare, medical research, patient data.

This experience taught me one thing: the value of a data platform is not measured by its technical complexity, but by the trust you can place in its data.

My approach

I’m not an engineer who just codes pipelines. I’m someone who understands business needs, proposes adapted solutions, and ensures they work in the long run.

In practice, this means:

• Scope before building — Understand the real problem, not the imagined one
• Favor simplicity — A maintainable solution beats a technical feat
• Ensure quality — Reliable, documented, actionable data
• Support teams — Transfer knowledge, train, enable autonomy

Background

My career follows a common thread: transforming raw data into actionable insights, in environments where errors have consequences.

2025 — Padoa (Occupational Health SaaS) Client data migration, high-volume pipeline industrialization. Platform squad work, coordination with business and clients.

2025 — Peasy (EdTech) Built the complete data stack. Integration pipelines, CRM governance, automated dashboards.

2021-2025 — Institut Jérôme Lejeune (Medical Research) 3 years structuring data repositories for a research institute: patients, biological samples, research data. Data warehouse implementation, governance, support for medical and scientific teams.

2017-2021 — AP-HP (Public Hospital) 4 years of clinical trial data management at France’s largest hospital system. Database administration, report automation.

Beliefs

Simple is better than complex. Readability counts.

These Zen of Python principles guide my work. I believe the best data systems are:

• Understandable by those who use them
• Maintainable by those who inherit them
• Reliable for those who make decisions with them

Tech hype doesn’t interest me. What interests me is data that truly serves those who need it.