Ontology Engineer

Andersen is hiring an Ontology Engineer for a project building a large scale Knowledge Graph and delivering AI-powered data and identity solutions.

 The company is a global technology provider delivering data analytics and AI-powered solutions that help organizations better understand customer behavior, optimize marketing performance, and make informed business decisions.

Responsibilities:

• Implementing and extending RDF/RDFS/OWL ontology schemas in the graph database - adding entity classes, properties.
• Building and maintaining SHACL validation shapes for post-load graph consistency checks.
• Contributing to the event-to-ontology transformation and derivation layer - building PySpark/Databricks pipelines that aggregate raw TV viewership.
• Implementing derivation logic specified by the Senior Ontologist and data science team.
• Writing production-quality Python - clean, well-tested, documented, and reusable by teammates. 
• Working with PySpark and Databricks to process and transform high-volume data as part of graph pipeline development. 
• Actively developing expertise in W3C semantic web standards, RDF-native graph databases.

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 Join us!  

Must-haves:

• Implementing and extending Samba's RDF/RDFS/OWL ontology schemas in the graph database - adding entity classes, properties, and constraints in a consistent.
• Building and maintaining SHACL validation shapes for post-load graph consistency checks.
• Writing efficient, well-structured SPARQL queries and graph traversals to support downstream data science.
• Working with PySpark and Databricks to process and transform high-volume data as part of graph pipeline development. 
• Applying embedding-based approaches (semantic similarity, vector search).
• Actively developing expertise in W3C semantic web standards, RDF-native graph databases.

Nice-to-haves:

• Hands-on experience with Amazon Neptune or Stardog - or equivalent RDF-native triplestore; exposure to data virtualization (Neptune Orion or Stardog Virtual Graphs).
• Working knowledge of PySpark and Databricks - particularly for large-scale event aggregation and transformation pipelines.
• Familiarity with embedding models, vector search, or semantic similarity - applied to entity matching, ontology alignment, or knowledge graph enrichment.
• Experience with LLM APIs or RAG-based approaches applied to information extraction, entity disambiguation, or schema mapping.