Semantic Modeler – Data & AI

Unleash Your Potential at L’Oréal’s Beauty Tech! Who Are We? For 115 years, L’Oréal, the world’s leading beauty player, has devoted itself to one thing only: fulfilling the beauty aspirations of consumers around the world.

For more than a century, L’Oréal has devoted itself solely to one business: Beauty. Present in 150 countries across five continents and with €42 billion consolidated sales, L’Oréal is the global industry leader. With 37 global beauty brands across four divisions, L’Oréal offers beauty for each covering all beauty categories and catering to all beauty desires. With the acquisition of the Australian brand Aēsop in 2023, the Group continues to expand its portfolio through targeted acquisitions as part of its drive to create the future of beauty.

Today, L’Oréal includes more than 2,000 tech professionals and is constantly growing. Beauty Tech is changing the game and leading the shift towards new consumer realities and a digital disruption. Championing Beauty Tech, we invent the beauty of the future while becoming the company of the future.

Beauty Tech is how we know our consumers intimately, augmenting their beauty journeys with unparalleled diverse and sustainable experiences. Beauty Tech equips the Group with the key assets it needs to conquer this new world, where Tech has become strategic. With this ambition, L’Oréal continues to recruit diverse, innovative, skilled and passionate minds in different tech domains such as Data, Digital, Cloud, Cyber Security, IT Architecture, DevOps, Applications and Infrastructure.

A Day in the Life of Semantic Modeler – Data & AI !

• Architect and own the Universal Semantic Layer, ensuring it serves as the single source of truth for all reports, self-service, and AI/ML initiatives.
• Enable AI Agency: Build the metadata “map” that allows AI Agents and LLMs to understand, browse, and analyse enterprise data with high precision and trust.
• Democratize Data Access: Design a self-service environment where non-technical business users can explore data using business terms rather than complex SQL.
• Ensure Cross-Project Consistency: Align the semantic definitions used in traditional BI reporting with the features used in AI/ML model training to prevent “metric drift”
• Standardize Semantic Governance: Define the workflows for how new metrics are proposed, validated, and deployed into the enterprise semantic layer.

In this role, You will.. Be Responsible For The Following

• Leading the design and evolution of the enterprise semantic layer, ensuring it is optimized for high-performance querying in BigQuery and seamless integration with downstream tools.
• Creating “Agent-Friendly” Metadata: Developing rich descriptions, tags, and relationship mappings within the semantic layer to guide AI Agents in autonomous data retrieval.
• Collaborating with Data Scientists and ML Engineers to ensure the semantic layer provides consistent, governed inputs for feature engineering and model inference.
• Developing and maintaining a “Metric Store” approach, allowing the business to define a KPI once and consume it everywhere (Dashboards, Python notebooks, AI Agents).
• Providing architectural guidance to BI and Delivery teams on how to build thin-client reports that rely entirely on the governed semantic model.
• Architecting the security and privacy layer within the semantic model, ensuring that PII and sensitive data are handled according to global compliance standards.
• Evaluating and implementing emerging “Headless BI” technologies to decouple data logic from the visualization layer, increasing architectural flexibility.
• Mentoring Data Modeler and Analytics Engineers on the shift from “flat-table” reporting to “object-oriented” semantic modeling.
• Leading the “Semantic Governance Board” to resolve naming conflicts and ensure that the bus.

What are we looking for?

• 10+ years of experience in Data Architecture, Software Engineering, or Information Management, with a specialized focus on building scalable semantic layers and metric stores.
• Extensive experience with Semantic Layer Technologies: Proficiency in tools like Looker (LookML)/dbt Semantic Layer/Cube/AtScale/Power BI (Tabular/DAX).
• Deep understanding of Data Modeling methodologies: Expert knowledge of Data Vault 2.0, Star Schema (Kimball), and Knowledge Graph/Ontology modeling.
• GCP Ecosystem Mastery: Proven experience architecting solutions on GCP, lever…