Data Engineer

Descrizione lavoro:

If you’ve worn a pair of glasses, we’ve already met.

We are a global leader in the design, manufacture, and distribution of ophthalmic lenses, frames, and sunglasses. We offer our industry stakeholders in over 150 countries access to a global platform of high-quality vision care products (such as the Essilor brand, with Varilux, Crizal, Eyezen, Stellest and Transitions), iconic brands that consumers love (such as Ray-Ban, Oakley, Persol, Oliver Peoples, Vogue Eyewear and Costa), as well as a network that offers consumers high-quality vision care and best-in-class shopping experiences (such as Sunglass Hut, LensCrafters, Salmoiraghi & Viganò and the GrandVision network), and leading e-commerce platforms.

Join our global community of over 190,000 dedicated employees around the world in driving the transformation of the eyewear and eyecare industry.

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Your #FutureInSight with EssilorLuxottica

Are you willing to pioneer new frontiers, foster inclusivity and collaboration, embrace agility, ignite passion, and make a positive impact on the world? Join us in redefining the boundaries of what’s possible

Your role

We are looking for a Data Engineer, Semantic and Knowledge Graph who will build and own the ontology behind our Operations data, and the mappings and validation that bring data into it. Operations data from a wide range of systems is being brought together into a single semantic layer. You keep that model coherent as new domains, geographies and entities arrive. The graph is the layer that analytics and AI systems will consume for years, and it needs someone whose responsibility it is. The role sits across two disciplines. You will do real data engineering, building the transformations that feed the graph and contributing to the wider pipeline work. You will also do modelling: representing products, sites, materials, orders and flows as the business understands them, rather than mirroring whichever source system the data arrived from.

We are hiring at mid-level for someone already competent who intends to become expert, with direct mentorship on semantic modelling and room to grow into the reference point for this work. If you have come to structured data modelling by another route than the semantic web stack, we would still like to hear from you: the modelling instinct is the part we cannot teach quickly, and the specific technologies are.

Main responsibilities:

• The ontology. Development and maintenance of the Operations domain model in OWL 2, applying established design patterns and keeping it coherent as scope expands.
• Mapping and transformation. The mappings that take relational and semi-structured data into graph form, including reconciling sources that arrive under different models.
• Validation. SHACL shapes, so that data entering the graph is checked against the model rather than trusted on arrival.
• Access patterns. SPARQL queries and access patterns that serve analytical and agentic consumption efficiently.
• Business alignment. Working with domain colleagues so the model reflects operational reality. An ontology that is formally elegant and semantically wrong is of no use to anyone.
• Shared engineering. Contribution to the broader ingestion work alongside the rest of the team.

Main requirements:

Essential:

• Solid data engineering experience, with fluent Python and SQL and practical familiarity with a modern data platform. Databricks experience is an advantage; competence transfers.
• Serious experience of structured data modelling, from any of several routes. Working knowledge of the semantic web stack (RDF, OWL, SPARQL) is the most direct one, whether gained professionally, in research or through substantial personal projects. We weigh the following equally: property graph work with Neo4j and Cypher or comparable technologies; metadata management, data cataloguing and lineage; substantial master data modelling; and the design of taxonomies and controlled vocabula