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# Senior Data & ai Transformation Lead - Semantic Layer H/F

- **Company:** Jobposting
- **Location:** France (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Architecture, Data Structures, Data Systems, Large Language Models, Data Strategy, Data Management, Virtual Agents
- **Published:** August 18, 2026
- **Apply:** https://www.hellowork.com/fr-fr/emplois/82363092.html

## About the Role

Expertise in strategic project management and C-level stakeholder management

 - Expertise in data strategy
 - Expertise in data architecture
 - Expertise in data solutions and technologies
 - Expertise in data management
 - Expertise in large-scale, federated and complex organizations, with cross-entity services
 - Expertise in AI, LLMs and Agentic AI architectures
 - English environment is mandatory

Compétences / Qualités indispensables :

Strategic project management, C-level stakeholder management, Data strategy, Data architecture, Data management, AI / LLMs / Agentic AI architectures

## Description

As part of an Agentic AI transformation program within a major international insurance group, the mission focuses on the definition of a semantic layer positioned between data sources and end users or consuming systems. The purpose of this semantic layer is to abstract underlying data structures such as tables, column names and joins into a business-friendly vocabulary including concepts, definitions, relationships and rules that can be interpreted by both humans and machines.

Main responsibilities / scope of work

The service is expected to support the definition of the semantic layer target approach and reference architecture, as well as the exploration of entities for an initial implementation approach based on selected Agentic AI use cases.

Deliverables expected

 - Governance framework
 - RACI
 - Meeting decks and meeting minutes for the definition of the semantic layer reference architecture
 - Entity exploration for a first approach to implementing the semantic layer in entities, based on selected Agentic use cases


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