R&D Data Lead: Global Analytics & Data Strategy

CEVA Logistics; Libourne; In loco; EUR 90.000 - 130.000; Tempo pieno

Descrizione del lavoro

Ceva Logistics is seeking a Data Lead R&D to define and deploy its data strategy across the organization. Reporting to the Digital Data & Analytics department, you will drive governance, quality, and value creation for R&D, leading an international team and positioning data as a key asset for analytics and AI initiatives.

You will align stakeholders and oversee data governance, quality, and regulatory compliance (VICH, veterinary GCP) while building data products to support R&D, analytics, and

Competenze

• Master's degree or Engineering degree in Data, Computer Science, or related field.
• Proven experience in international multi-site team management.
• Strong expertise in data strategy, governance, data quality, and data products.
• Experience working with Data Engineering, Snowflake, SQL and business stakeholders.
• Knowledge of regulated environments and data integrity principles (GCP, ALCOA+).

Mansioni

• Define and implement the R&D data strategy across business units and sites.
• Lead the data roadmap: governance, MDM, data catalog, data products, Snowflake platform.
• Establish the data platform as a trusted source for analytics and AI.
• Manage and develop an international Data team across locations.

Conoscenze

Leadership

Team management

Stakeholder management

Fluent English

French appreciated

Formazione

Master's or Engineering degree in Data/CS

Strumenti

Snowflake

SQL

MDM

Data Catalog

CDISC standards

Descrizione del lavoro

Ceva Logistics is seeking a Data Lead R&D to define and deploy its data strategy across the organization. Reporting to the Digital Data & Analytics department, you will drive governance, quality, and value creation for R&D, leading an international team and positioning data as a key asset for analytics and AI initiatives.

You will align stakeholders and oversee data governance, quality, and regulatory compliance (VICH, veterinary GCP) while building data products to support R&D, analytics, and