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# Data Architect Lead H/F

- **Company:** Jobposting
- **Location:** France (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Architecture, Data Structures, Software Design Documents, Large Language Models, Collibra, Data Management, Virtual Agents, Databricks
- **Published:** August 19, 2026
- **Apply:** https://www.hellowork.com/fr-fr/emplois/82399059.html

## About the Role

Expertise in data architecture, including data modelling, ontologies, graphs, data product, and data mesh

 - Expertise in producing reference architecture documents
 - Expertise in low-level design of data products, ontologies, and related assets
 - Expertise in data management
 - Expertise in the landscape of existing market data solutions and technologies, including Databricks, MS Fabric, and Collibra
 - Expertise in large-scale, federated, and complex organizations, with cross-entity services
 - Expertise in AI, LLMs, and Agentic AI architectures
 - English-speaking environment is mandatory

Compétences / Qualités indispensables :

Data architecture and data modelling, Ontologies and semantic layer design, Data products and data mesh, Reference architecture documentation, AI, LLMs and Agentic AI architectures, English mandatory

## Description

Within an international insurance group's Agentic AI transformation, the semantic layer is identified as a layer sitting between data sources and the end users or systems that consume them. Its role is to abstract data structures (tables, column names, joins) into a business-friendly vocabulary (concepts, definitions, relationships, rules) that humans and machines can interpret. It is presented as an important component enabling LLM-based agents to interpret data in a trustworthy manner and to support the scalability of Agentic AI across multiple use cases.

Service deliverables

 - Entity exploration to define a first approach for implementing the semantic layer in entities, based on selected Agentic AI use cases
 - Low-level design of reusable semantic assets, including business vocabulary, ontologies, business process, data product mapping, and governance aligned with standards defined at group level
 - Demonstration of value, feasibility validation, and learning through hands-on experimentation to enrich group standards


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