Data Engineering Lead- R&D pharma- M/F

Pierre Fabre is accelerating its transformation toward a more digital, data-driven and AI-enabled R&D organization.

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To support this ambition, we are creating a new Data Engineering Lead position in Toulouse (Langlade, 31).

You will be part of the R&D Data Engineering team that operates the data platform and delivers trusted, reusable data products for Data Science, Biometry and all other R&D functions. The role combines team leadership, technical ownership and hands-on delivery. It is accountable for turning fragmented scientific and operational data into governed, production-grade assets that accelerate R&D decisions and use cases.

Your role within a pioneering company in full expansion:1. Lead the Data Engineering team

• Build, organize and lead an internal team of data engineers, complemented by external partners when required.
• Set clear priorities, allocate capacity and establish an explicit delivery backlog jointly with R&D stakeholders and Data Science leads.
• Coach engineers, raise engineering standards, develop skills and create a culture of accountability, collaboration and continuous improvement.

2. Own data-platform engineering and operations

• Take technical ownership of the R&D data platform in its AWS landing-zone environment, from implementation through reliable day-to-day operation and evolution.
• Define and enforce reference architectures, reusable patterns and engineering standards for ingestion, transformation, orchestration, storage and data serving.
• Implement infrastructure as code, CI/CD, automated testing, environment separation, observability, incident response and disaster-recovery practices.

3. Deliver trusted data products for R&D

• Partner with scientists, Data Science, Biometry, Business Analysts and R&D domain teams to translate needs into prioritised data products and services.
• Design, build and operate batch, incremental, API-based and event-driven pipelines across laboratory, preclinical, clinical, external-partner and operational sources.
• Create curated, documented and reusable datasets with clear schemas, ownership, quality rules, access conditions, SLAs and lifecycle management.
• Provide governed access through SQL, APIs, notebooks and other fit-for-purpose interfaces, enabling analytics, AI and scientific applications.

4. Embed quality, security and compliance by design

• Embed automated data-quality controls, lineage, metadata capture, auditability and reproducibility into engineering workflows.
• Apply role-based and data-level access controls, encryption, retention and classification requirements in partnership with Security, Data Governance, Quality, Legal and the DPO.
• Support GxP-qualified workloads, GDPR obligations and HDS-compliant EU data residency where required.

5. Shape the engineering roadmap and ecosystem

• Contribute to platform technology selection and lead the engineering roadmap from MVP to scale.
• Evaluate and integrate relevant technologies across AWS and modern data platforms such as Databricks, Snowflake or SageMaker Unified Studio, based on the selected target architecture.
• Ensure interoperability with the existing R&D estate, including Azure-hosted sources, GitHub, Veeva, electronic laboratory notebooks, statistical computing environments and external CRO/partner channels.

This position is compatible with teleworking up to 2 days a week according to company rules.

We offer an attractive remuneration/benefits package:

• Incentives, profit-sharing, Pierre Fabre shareholding with matching contribution, health and provident insurance, 16 days of holidays (RTT) in addition to 25 days of personal holidays, public transport participation...

Your skills at the service of innovative projects:

• Significant experience in data engineering, including ownership of production-grade cloud data platforms and large-scale data pipelines.
• Demonstrated experience leading and developing data engineers
• Strong experience with AWS and at least one modern data platform or lakehouse/warehouse ecosystem such as Databricks, Snowflake or SageMaker Unified Studio.
• Advanced practical skills in SQL and Python, data modelling, ETL/ELT, orchestration, APIs and distributed data processing.
• Experience with cloud object storage, open table/file formats, metadata, data quality, lineage and secure data access.
• Experience implementing engineering practices including Git, CI/CD, infrastructure as code, automated testing, monitoring and incident management.
• Ability to engage with scientific and business stakeholders, convert ambiguous needs into executable delivery plans and communicate technical trade-offs