Senior Ontologist - Knowledge Graph & Identity

Samba; Amsterdam; On-site; EUR 110,000 - 160,000; Full time

Job summary

Samba in Amsterdam seeks an experienced Ontology Engineer to own the design, governance, and evolution of Samba's semantic data models as the domain authority for its knowledge graph. This hands-on role includes ontology design, SHACL validation, and building production pipelines with data engineering peers.

You will lead ontology reviews with cross-functional teams, drive trade-offs between expressivity and performance, and mentor junior engineers and scientists while applying AI and

Qualifications

• 5+ years in ontology engineering or knowledge graph development.
• Proven production ontologies at scale.
• Strong W3C standards expertise (RDF/RDFS/OWL).
• Experience with graph databases and SPARQL.
• Ability to defend modeling decisions in design reviews.

Responsibilities

• Own Core Ontologies: design and versioning of core ontologies in RDF/RDFS/OWL and set standards.
• Validate and Define Schemas: author SHACL shapes for post-load validation and derived attributes.
• Define Derivation Logic: establish scoring logic for viewership events and graph attributes.
• Build Graph Pipelines: production-quality pipelines in Python and SPARQL; entity resolution and record linkage.
• Enrich and Apply AI: integrate third-party data; apply embeddings and LLM approaches for ontology mapping.
• Lead Design Reviews: articulate trade-offs among expressivity, scalability, and query performance.
• Mentor and Teach: mentor Ontology Engineers and data scientists; lead internal talks on knowledge graphs.

Skills

Ontology engineering

Semantic data modeling

Knowledge graph development

Explain trade-offs to non-specialists

Python data pipelines

Entity resolution / deduplication

SHACL design patterns

Education

Bachelor's degree in Computer Science, Information Science, Computational Linguistics, Mathematics, or related field

Tools

SPARQL

Python

SHACL

RDF/RDFS/OWL

Amazon Neptune

Stardog

GraphDB

Jena

PySpark

Databricks

Job description

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.

About the Role

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.

What You'll Do

• 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.

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