5 months ago
Paris, FranceStaff+
Responsibilities
- Define and implement data engineering standards, best practices, and development frameworks.
- Build tools and automation to enforce standards and ensure quality at scale.
- Develop data pipelines and critical implementations on Databricks.
- Drive CI/CD, testing, automation, and broader industrialization of data engineering.
- Lead and animate the Data Engineering community of practice and promote knowledge sharing.
- Evangelize tools, frameworks, and best practices across data engineering teams.
- Collaborate with platform, data, and business teams on consistent and scalable solutions.
- Provide technical expertise and guidance on complex data engineering projects.
Requirements
- Strong experience in data engineering, including hands-on development on Databricks.
- Experience designing and implementing data engineering frameworks or standards.
- Ability to combine hands-on technical work with leadership and influence.
- Experience working in matrix environments and coordinating multiple data engineering teams.
- Strong knowledge of cloud platforms and modern data architectures, with AWS experience listed as a plus.
- Experience with CI/CD tools and practices, including GitHub and GitHub Actions.
- Understanding of data pipelines, automation, and industrialization practices.
- Familiarity with Generative AI topics is a plus.
- Strong communication skills and ability to drive adoption across teams.
- Fluency in English.
Benefits
- Key role shaping data engineering standards at enterprise scale.
- Opportunity to work on modern data platforms including Databricks, AWS, and AI.
- Mix of hands-on engineering and technical leadership.
- Strong impact on how data and AI solutions are built and scaled across Ipsen.
- Hybrid work arrangement indicated by the #LI-HYBRID posting tag.
Tech Stack
Categories
Data Engineering
