Senior Data Engineer: Medical AI Data Architect, Hybrid

DeepHealth; Paris; Hybride; EUR 60 000 - 65 000; Plein temps

Résumé du poste

DeepHealth is seeking a Senior Data Manager who will own data topics across products, including dataset creation, data export, annotation workflows, and performance monitoring.

You will design reusable tooling, pipelines, dashboards, and automation; mentor junior staff; and collaborate with radiologists, product managers, AI engineers, clinical researchers, and data engineers to advance medical product development.

Qualifications

• Master's degree in a health-related or scientific field.
• 5+ years in data management, data engineering, or data science.
• Proficiency in Python and databases (BigQuery, MongoDB).
• Ability to structure, document, industrialize, and optimize data processes and tools.
• Experience writing automated tests and working with code versioning.
• Knowledge of medical imaging and AI data requirements.

Responsabilités

• Own data topics across products (dataset creation, data export, annotation workflows, performance monitoring).
• Design and maintain reusable tooling, pipelines, dashboards, and automation.
• Make structuring decisions on implementation, tooling, and data architecture.
• Surface actionable insights from the data lake for product, AI, and clinical stakeholders.
• Drive data targeting and collection; build medically consistent datasets.
• Establish annotation guidelines and training for quality.
• Mentor junior data team members through guidance and knowledge sharing.
• Write automated tests and apply versioning and review standards.

Connaissances

5+ years data management / data eng /

Formation

Master's degree in health-related or scientific field

Outils

Python

BigQuery

MongoDB

Git

Automated testing

Description du poste

DeepHealth is seeking a Senior Data Manager who will own data topics across products, including dataset creation, data export, annotation workflows, and performance monitoring.

You will design reusable tooling, pipelines, dashboards, and automation; mentor junior staff; and collaborate with radiologists, product managers, AI engineers, clinical researchers, and data engineers to advance medical product development.