Ontology Engineer-Knowledge Graph & Identity

What You'll Do:

Ontology Implementation & Validation

Implement and extend Samba's RDF/RDFS/OWL ontology schemas in the graph database - adding entity classes, properties, and constraints in a consistent, governed way under the direction of the Senior Ontologist

Build and maintain SHACL validation shapes for post-load graph consistency checks; identify and triage data quality and schema violations

Support ontology versioning, change log documentation, and consistency checking across schema updates

Write efficient, well-structured SPARQL queries and graph traversals to support downstream data science and product use cases

Event-to-Ontology Derivation Pipelines

Contribute to the event-to-ontology transformation and derivation layer - building PySpark/Databricks pipelines that aggregate raw TV viewership and web activity events into durable graph attributes (genre affinity, brand affinity, topic affinity, viewing summaries, lifecycle signals)

Implement derivation logic specified by the Senior Ontologist and data science team; validate outputs against SHACL shapes before graph load

Support incremental refresh and update logic aligned with the graph's batch refresh cadence

Technical Contribution

Write production-quality Python - clean, well-tested, documented, and reusable by teammates

Work with PySpark and Databricks to process and transform high-volume data as part of graph pipeline development

Apply embedding-based approaches (semantic similarity, vector search) to entity matching and ontology alignment tasks

Contribute to team tooling, documentation, and reusable components that improve knowledge graph development efficiency

Collaboration & Growth

Partner closely with data engineering on pipeline design, data quality, and incremental ingestion patterns feeding the materialized graph substrate

Participate in ontology design reviews and cross-functional working groups

Work with product and operations teams to understand use case requirements and translate them into graph schema updates

Actively develop expertise in W3C semantic web standards, RDF-native graph databases, and entity resolution under the guidance of the Senior Ontologist

Who You Are:

Must-Haves

2–4 years of hands-on experience in knowledge graph development, semantic data modeling, ontology engineering, or a closely related field

Working knowledge of W3C semantic web standards: RDF, RDFS, OWL, and SPARQL - with practical experience querying or building in at least one triplestore or graph database

Familiarity with SHACL or equivalent constraint and validation frameworks for graph data quality

Strong Python skills - clean, readable, production-quality code with testing and documentation

Solid understanding of data modeling fundamentals - entity-relationship design, taxonomies, hierarchies, and how to represent complex real-world relationships in structured form

Familiarity with entity resolution or data matching concepts - understanding of why the same real-world entity appears under different identifiers across data sources

Bachelor's degree required in Computer Science, Information Science, Mathematics, or a related field; Master's preferred

Detail-oriented and proactive about flagging data quality issues and schema inconsistencies

Strongly Preferred

Hands-on experience with Amazon Neptune or Stardog - or equivalent RDF-native triplestore; exposure to data virtualization (Neptune Orion or Stardog Virtual Graphs) a plus

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

Domain knowledge in media, entertainment, or ad tech - content metadata, advertising entities, TV viewership data, or audience/identity data

Exposure to identity resolution, probabilistic record linkage, or device graph approaches