DATA MODELER (F/M)

About Sonepar

Sonepar is an independent family-owned company standing as the world leader in B-to-B distribution of electrical equipment, solutions, and services. In 2024, Sonepar achieved sales of €32.5 billion. Present in 40 countries with a dense network of brands, the Group is leading an ambitious transformation to make its customers’ lives easier providing them with an omnichannel experience and sustainable solutions in the building, industry, and energy markets. 

Sonepar’s 46 000 associates are committed to accelerating the world’s electrification and driven by a shared Purpose: Powering Progress for Future Generations.

www.sonepar.com

 

 

What success looks like

 

Modelling Standards & Quality

• Enterprise-wide modelling standards, playbooks, and quality gates are clearly defined, adopted, and consistently enforced.
• AI-assisted and agentic capabilities are embedded into modelling practices to accelerate design, review, documentation, and quality control while preserving governance.
• Continuous, automated quality checks detect non-compliance, anti-patterns, and structural risks early in the lifecycle.

 

Domain & Architecture Alignment

• Domains and sub-domains are coherently designed and aligned with group data architecture principles and the global data roadmap.
• Modelling decisions explicitly address scalability, interoperability, performance, governance, cost (FinOps), and AI-readiness.
• AI-driven lineage and impact analysis are used to anticipate downstream effects of modelling decisions across platforms and domains.

 

Governance & Business Enablement

• Data models provide a shared business language, supported by a robust glossary, data dictionary, lineage, and semantic consistency.
• Modelling standards explicitly support AI and advanced analytics consumption, including semantic layers, feature reuse, and explainability requirements.
• Governance artefacts are actively used and kept up to date through automation and agent-based workflows.

 

Community Leadership & Design Authority

• A structured, active Data Modelling Practice is in place, with high-quality, consistent outputs across teams and geographies.
• A Design Authority effectively reviews critical models, balancing AI-assisted insights with human accountability and architectural judgment.
• Collective modelling maturity continuously improves through mentoring, reviews, and shared practices.

 

Tooling, Automation & Industrialization

• Tooling and automation industrialize modelling practices, improving delivery speed, traceability, and reliability.
• Agent-based workflows support schema management, version control, lineage inference, and documentation at scale.
• Data models are production-ready and reusable across countries, platforms, and AI-driven use cases.

 

 

The experience you bring:

 

Data Modelling & Architecture Expertise

• Deep expertise in conceptual, logical, and physical data modelling with a strong governance and quality mindset.
• Solid background in data architecture and enterprise architecture alignment in large, federated environments.
• Ability to design data models that are scalable, interoperable, and fit for analytics, operational, and AI-driven use cases.

 

AI, Agentic & Semantic Capabilities

• Hands-on experience using AI-augmented or agentic tools for modelling, documentation, lineage, or model validation.
• Strong understanding of semantic modelling, metadata management, and their role in enabling GenAI, feature stores, and autonomous data products.
• Ability to define clear guardrails for AI-assisted modelling, ensuring explainability, traceability, and accountability.

 

Standards, Methods & Governance

• Proven experience defining and deploying modelling standards, playbooks, reusable templates, and quality frameworks at scale.
• Strong command of governance artefacts using Purview (glossary, data dictionary, lineage) and thei