Data & AI Engineer

Le contenu du poste est libellé en anglais car il nécessite de nombreuses interactions avec nos filiales à l’international, l’anglais étant la langue de travail.

This job offer is accessible to all, regardless of gender.

Job title: Data & AI Engineer

Location: Paris, France

Work Arrangements: Hybrid ( Monday, Tuesday, Wednesday onsite )

About the job

As a senior data & AI engineer within our Digital R&D team, you will help revolutionize how Sanofi discovers and delivers life‑saving medicines through AI and digital innovation. Ready to get started?

Based in Paris, France, you will be part of a dynamic team driving Sanofi's digital transformation in R&D. We are building outstanding Digital R&D products that participate in new drug discovery together with scientists, in trial efficiency and cycle time reduction, leveraging Data Analytics and Artificial Intelligence. Join the first biopharma company powered by AI at scale, where your expertise will directly impact how we deliver treatments to patients worldwide.

About Sanofi:

We’re an R&D-driven, AI-powered biopharma company committed to improving people’s lives and delivering compelling growth. Our deep understanding of the immune system – and innovative pipeline – enables us to invent medicines and vaccines that treat and protect millions of people around the world. Together, we chase the miracles of science to improve people’s lives.

Main responsibilities:

Design and implement end-to-end data pipelines that transform raw data into valuable insights, ensuring scalability and reliability in cloud environments

Develop and optimize data models with a focus on query performance and efficient workloads

Collaborate with cross-functional teams to translate business requirements into technical solutions, defining clear interface contracts between data products and applications

Ensure data quality, standardization, observability, and governance across systems, aligning with industry compliance standards and data privacy requirements

Automate data ingestion processes and monitoring systems to track operational KPIs, troubleshoot issues, and maintain pipeline health

Build and maintain ETL processes and machine learning workflows, providing clean AI-ready data for downstream applications

Contribute to MLOps/AIOps practices including model deployment pipelines, model performance monitoring, and reproducibility of ML experiments in production environments

Actively contribute to transversal data engineering best practices, including design patterns, CI/CD integrations, containerized deployments, and participate in peer review of code and technical documentation standards

Provide technical guidance to team members, ensuring adherence to coding standards and best practices

Coordinate project timelines and deliverables with cross-functional teams to ensure alignment with business goals

About you

Experience:

Strong track record in designing and implementing data pipelines and data warehouse solutions in cloud environments

Hands‑on experience with data modeling, ETL/ELT processes, and pipeline orchestration in production settings

Background working in cross-functional teams, translating business needs into technical solutions

Experience working within compliance (e.g.: quality, regulatory, data privacy, GxP) and cybersecurity requirements

Experience in the healthcare industry is a strong plus

Technical Skills:

Strong proficiency in SQL and data warehousing platforms (Snowflake preferred)

Hands‑on experience with ETL tools (IICS or equivalent), data transformation tools (dbt preferred), and Python for data processing

Strong experience with cloud platforms (AWS preferred) including orchestration frameworks (Airflow preferred)

Good knowledge of CI/CD practices (GitHub Actions preferred), version control (Git), and infrastructure as code principles (Terraform preferred)

Experience in designing and implementing engineering patterns and technical standards

Good knowledge of logging and monitoring tools such as Datadog, Grafana

Familiarity with containerization (Docker) and container orchestration (Kubernetes/Openshift a plus)

Possessing relevant cloud certifications (AWS, Snowflake, IICS) is a plus

Soft Skills:

Collaborative mindset with strong problem-solving abilities

Self‑motivated and able to take initiative in a fast‑paced environment

Effective communication skills to work with both technical and business stakeholders, with the ability to articulate insights and influence decision‑making

AI-assisted engineering as a default: AI tooling is a default expectation, not an experiment. Use it across coding, code review, documentation, and operations. We track