The Role
Own the design, governance, and production development of semantic data models, ontologies, and knowledge graph pipelines. Build RDF, OWL, SHACL, Python, and SPARQL solutions; define graph derivation logic; perform entity resolution; integrate third-party and AI-based enrichment; and support GraphRAG systems. Lead design reviews, establish modeling standards, collaborate with engineering and data science teams, and mentor ontology engineers and junior data scientists.
Summary Generated by Built In
Samba connects the advertising industry. Today, a trillion dollars of media flows through platforms that don’t talk to each other, and each one measures its own results. We built the independent alternative: consented first-party data and AI that give the world’s biggest advertisers, publishers, and platforms one clear view across all of it.
The next era of advertising will run on AI agents, and those agents will need data they can trust. We’re building that foundation, and the agents that act on it. Join us and help build what the industry runs on.
You own the design, development, and governance of the semantic data models and ontological frameworks at the foundation of Samba's knowledge graph, as the domain authority on how Samba represents its most important entities. It's a hands-on role: you design ontologies, write SPARQL, and build knowledge graph pipelines with data engineering and data science peers.
- Own Core Ontologies: Own the design and versioning of core ontologies in RDF, RDFS, and OWL, and set design standards, change management, and alignment with World Wide Web Consortium (W3C) and industry schemas.
- Validate and Define Schemas: Author SHACL shapes for post-load validation, and define the derived-attribute schemas (genre, brand, and topic affinity, lifecycle signals, viewing summaries) that turn raw events into durable graph attributes.
- Define Derivation Logic: Define the scoring logic that turns viewership and web events into graph signals, co-own pipeline design with data engineering, and decide what belongs in the graph versus the data lake.
- Build Graph Pipelines: Build and maintain production-quality knowledge graph pipelines in Python and SPARQL, including entity resolution and record linkage that map real-world entities (titles, devices, audiences, advertisers) to canonical graph nodes.
- Enrich and Apply AI: Integrate third-party data into the graph, apply embedding-based and large language model (LLM) approaches to ontology mapping and entity disambiguation, and support the embedding pipelines behind GraphRAG solutions.
- Lead Design Reviews: Lead ontology design reviews with product, data engineering, and data science, articulating trade-offs among expressivity, scalability, and query performance, and translate business requirements into graph data model decisions.
- Mentor and Teach: Formally mentor Ontology Engineers and junior data scientists on semantic modeling, SHACL design patterns, and graph best practices, and lead internal talks and workshops on ontology and knowledge graph topics.
What You Bring
- Bachelor's degree in Computer Science, Information Science, Computational Linguistics, Mathematics, or a related field, or equivalent practical experience; a Master's or PhD is strongly preferred.
- 5+ years of hands-on experience in ontology engineering, semantic data modeling, or knowledge graph development, with a demonstrable track record of production ontologies at scale.
- Deep expertise in W3C semantic web standards (RDF, RDFS, OWL, SPARQL 1.1, SHACL), with hands-on experience building and validating graph schemas in a production triplestore (Amazon Neptune, Stardog, GraphDB, Jena, or equivalent).
- Strong, production-quality Python for data pipelines and graph processing, and a first-principles understanding of description logics, ontology design patterns, and the trade-offs between OWL expressivity and scalability.
- Hands-on experience with entity resolution, record linkage, or deduplication at scale, and the communication skills to defend modeling decisions in design reviews and explain trade-offs to non-specialists.
- Strongly preferred: hands-on experience with Amazon Neptune or Stardog, including data virtualization over data lake sources, designing derivation logic that converts raw behavioral events into graph attributes, and PySpark and Databricks.
- Also strongly preferred: media, entertainment, or adtech domain knowledge (TV viewership, device graphs, identity resolution); familiarity with EIDR, Schema.org, or DDEX; embedding models, vector databases, and GraphRAG; and graph neural network approaches as a plus.
Samba is an equal opportunity employer.
Your personal information
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Use of AI in hiring
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Skills Required
- Bachelor's degree in Computer Science, Information Science, Computational Linguistics, Mathematics, a related field, or equivalent practical experience
- 5+ years of hands-on experience in ontology engineering, semantic data modeling, or knowledge graph development
- Demonstrable track record of production ontologies at scale
- Deep expertise in RDF, RDFS, OWL, SPARQL 1.1, and SHACL
- Hands-on experience building and validating graph schemas in a production triplestore
- Strong production-quality Python experience for data pipelines and graph processing
- Understanding of description logics, ontology design patterns, and OWL expressivity and scalability trade-offs
- Hands-on experience with entity resolution, record linkage, or deduplication at scale
- Communication skills for defending modeling decisions and explaining trade-offs to non-specialists
- Master's or PhD degree
- Hands-on experience with Amazon Neptune or Stardog, including data virtualization over data lake sources
- Experience designing derivation logic that converts behavioral events into graph attributes
- Experience with PySpark and Databricks
- Media, entertainment, or adtech domain knowledge
- Familiarity with EIDR, Schema.org, or DDEX
- Experience with embedding models, vector databases, and GraphRAG
- Experience with graph neural networks
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The Company
What We Do
Samba is a media-intelligence company that combines consented first-party viewership data from streaming, linear TV, the web, and video games with AI to map content, audiences, and outcomes. Its platform gives advertisers, publishers, and media platforms insight into audience behavior and supports campaign planning, targeting, measurement, and optimization across channels. Samba’s data and analytics help clients understand audiences and make marketing decisions.
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