Samba TV

Delivers TV audience measurement and insights

Ontology Engineer-Knowledge Graph & Identity

Data ManagementConfirmed live in the last 24 hours

Full-timeNew York, NY, USA$150k - $180k /yrIn-person

About the Job

Requirements

Two to four 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, including clean, readable, production-quality code with testing and documentation.
Solid understanding of data modeling fundamentals, including entity-relationship design, taxonomies, hierarchies, and representing complex real-world relationships in structured form.
Familiarity with entity resolution or data matching concepts, including understanding why the same real-world entity appears under different identifiers across data sources.
Bachelor's degree in Computer Science, Information Science, Mathematics, or a related field.
Detail-oriented and proactive about flagging data quality issues and schema inconsistencies.

Responsibilities

Implement and extend Samba's RDF/RDFS/OWL ontology schemas in the graph database by 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, and 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.
Contribute to the event-to-ontology transformation and derivation layer by building PySpark/Databricks pipelines that aggregate raw TV viewership and web activity events into durable graph attributes, including genre affinity, brand affinity, topic affinity, viewing summaries, and lifecycle signals.
Implement derivation logic specified by the Senior Ontologist and data science team, and validate outputs against SHACL shapes before graph load.
Support incremental refresh and update logic aligned with the graph's batch refresh cadence.
Write production-quality Python that is 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, including semantic similarity and vector search, to entity matching and ontology alignment tasks.
Contribute to team tooling, documentation, and reusable components that improve knowledge graph development efficiency.
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.

Desired Qualifications

Hands-on experience with Amazon Neptune or Stardog, or an equivalent RDF-native triplestore; exposure to data virtualization such as Neptune Orion or Stardog Virtual Graphs is 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, including content metadata, advertising entities, TV viewership data, or audience/identity data.
Exposure to identity resolution, probabilistic record linkage, or device graph approaches.
A master's degree.

Salary Range: 150000 to 180000 (Currency: USD) (Pay period: per-year-salary)

Samba 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 & 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

Samba 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.

About the Company

Samba TV

Delivers TV audience measurement and insights

Samba TV collects and analyzes TV viewing data to help advertisers, broadcasters, and content creators understand what people watch. It embeds its technology in about 35 million smart TVs across 20 brands, creating a large viewership database and a household identity graph. The system works by using a proprietary multi-source panel from participating TVs to gather viewing activity, then turning that data into insights and analytics that clients can use to plan ads, optimize content, and measure performance. What sets Samba TV apart is the size and scope of its data: it claims the largest end-to-end viewership dataset and identity graph in the industry, with a panel hundreds of times larger than typical measurements and broad brand partnerships, while offering privacy controls for viewers who opt in or out. The company aims to provide data-driven, actionable insights to improve advertising effectiveness and content delivery across the global smart TV market.

Company size: 201-500
Company stage: Debt Financing
Total funding: $75.7M
Headquarters: San Francisco, CA
Founded: 2008

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