Strategic Data Architect — AI-Ready Platform & Cloud

Rexel Group

Paris

Hybride

EUR 110 000 - 140 000

Plein temps

14 jours+
Générateur de candidature

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Avantages offerts par ce poste

Health insurance
Retirement plans
Transport allowance
Meal allowance
Profit sharing

Résumé du poste

Rexel Group in Paris is seeking an experienced Data Architect to drive our Data Platform and enable advanced analytics and AI initiatives across Europe. You will define roadmaps, ensure data models and pipelines are scalable, secure, and well-governed, and partner with IT managers in several countries to deliver value for internal customers.

You will lead data architecture discussions, establish standards, and stay hands-on with conceptual, logical, and physical data modeling while collaborating

Qualifications

  • Engineering degree or equivalent with 10+ years of experience.
  • Proven experience delivering large-scale data projects and collaborating with offshore teams.
  • Strong knowledge of data modelling, data integration, and cloud data platforms.

Responsabilités

  • Drive evolution of Rexel’s enterprise data architecture.
  • Define data strategies, data flows, and data models across domains.
  • Lead architecture governance meetings with country IT services and stakeholders.
  • Ensure data quality, metadata management, and observability across pipelines.
  • Collaborate with central hubs to enable AI-ready data platforms.

Connaissances

Data architecture
Data modeling
Snowflake
Power BI
SQL
Leadership
Offshore collaboration
Governance

Formation

Engineering degree

Outils

DBT
Atlan
Snowflake

Description du poste

Rexel Group in Paris is seeking an experienced Data Architect to drive our Data Platform and enable advanced analytics and AI initiatives across Europe. You will define roadmaps, ensure data models and pipelines are scalable, secure, and well-governed, and partner with IT managers in several countries to deliver value for internal customers.

You will lead data architecture discussions, establish standards, and stay hands-on with conceptual, logical, and physical data modeling while collaborating

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