Ontology Engineer-Knowledge Graph & Identity

San Francisco, CaliforniaEngineering – Data Science & Analytics /International Full Time Employee /On-siteSamba is a media intelligence company. We know what the world is watching, reading, and thinking about — in real time, at scale, across every screen. Our data exists with the consent of over a billion people, organized into the most complete picture of consumer attention ever built. The biggest brands in the world use that picture to make smarter decisions. We think it’s the most interesting data asset on the planet, because it’s the most culturally relevant. As an Ontology Engineer on Samba TV's Knowledge Graph & Identity team, you will build, maintain, and extend the knowledge graph schemas, derivation pipelines, and graph data models that underpin Samba's measurement and audience intelligence products. Working closely with the Senior Ontologist and peer data scientists, you will implement ontological frameworks in production, contribute to entity resolution and data enrichment pipelines, and help ensure the graph layer remains accurate, consistent, and production-ready.This is a hands-on technical role. You are expected to write clean, production-quality Python and SPARQL, take ownership of well-scoped graph work streams, and grow your depth in semantic modeling under the guidance of senior team members.This role reports to the Data Science Manager, Knowledge Graph & Identity.What You'll Do:Ontology Implementation & ValidationImplement 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 OntologistBuild and maintain SHACL validation shapes for post-load graph consistency checks; identify and triage data quality and schema violationsSupport ontology versioning, change log documentation, and consistency checking across schema updatesWrite efficient, well-structured SPARQL queries and graph traversals to support downstream data science and product use casesEvent-to-Ontology Derivation PipelinesContribute 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 loadSupport incremental refresh and update logic aligned with the graph's batch refresh cadenceTechnical ContributionWrite production-quality Python - clean, well-tested, documented, and reusable by teammatesWork with PySpark and Databricks to process and transform high-volume data as part of graph pipeline developmentApply embedding-based approaches (semantic similarity, vector search) to entity matching and ontology alignment tasksContribute to team tooling, documentation, and reusable components that improve knowledge graph development efficiencyCollaboration & GrowthPartner closely with data engineering on pipeline design, data quality, and incremental ingestion patterns feeding the materialized graph substrateParticipate in ontology design reviews and cross-functional working groupsWork with product and operations teams to understand use case requirements and translate them into graph schema updatesActively develop expertise in W3C semantic web standards, RDF-native graph databases, and entity resolution under the guidance of the Senior OntologistWho You Are: Must-Haves2–4 years of hands-on experience in knowledge graph development, semantic data modeling, ontology engineering, or a closely related fieldWorking knowledge of W3C semantic web standards: RDF, RDFS, OWL, and SPARQL - with practical experience querying or building in at least one triplestore or graph databaseFamiliarity with SHACL or equivalent constraint and validation frameworks for graph data qualityStrong Python skills - clean, readable, production-quality code with testing and documentationSolid understanding of data modeling fundamentals - entity-relationship design, taxonomies, hierarchies, and how to represent complex real-world relationships in structured formFamiliarity with entity resolution or data matching concepts - understanding of why the same real-world entity appears under different identifiers across data sourcesBachelor's degree required in Computer Science, Information Science, Mathematics, or a related field; Master's preferredDetail-oriented and proactive about flagging data quality issues and schema inconsistenciesStrongly PreferredHands-on experience with Amazon Neptune or Stardog - or equivalent RDF-native triplestore; exposure to data virtualization (Neptune Orion or Stardog Virtual Graphs) a plusWorking knowledge of PySpark and Databricks - particularly for large-scale event aggregation and transformation pipelinesFamiliarity with embedding models, vector search, or semantic similarity - applied to entity matching, ontology alignment, or knowledge graph enrichmentExperience with LLM APIs or RAG-based approaches applied to information extraction, entity disambiguation, or schema mappingDomain knowledge in media, entertainment, or ad tech - content metadata, advertising entities, TV viewership data, or audience/identity dataExposure to identity resolution, probabilistic record linkage, or device graph approaches$150,000 - $180,000 a yearSamba is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We strive to empower connection with one another, reflect the communities we serve, and tackle meaningful projects that make a real impact.Samba may collect personal information directly from you, as a job applicant, Samba may also receive personal information from third parties, for example, in connection with a background, employment or reference check, in accordance with the applicable law. For further details, please see Samba's Applicant Privacy Policy. For residents of the EU , Samba Inc. is the data controller. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.