Capital One
Manager, Ontology & Data Modeling
$150K–$205K
Chicago, IL +3 · Manager · Full-time · Posted Aug 31, 2026
- Location
- Chicago, IL
McLean, VA
Richmond, VA
New York, NY - Experience
- At least 4 years in a metadata field; at least 4 years’ experience or training in W3C standards and ontology/linked data tools; at least 4 years’ experience or training with SQL or SPARQL.
- Education
- Bachelor’s degree in information science, computer science, engineering, library science, ontology, semantics, or computational linguistics.
About the job
Leads the development, governance, and integration of enterprise ontologies and data models to support Capital One’s data strategy and semantic capabilities.
What you'll do
- Manage teams that develop, implement, and govern ontologies and optimized data models, and set related standards and guidelines.
- Own the ontology and data modeling roadmap; align stakeholders and communicate vision, risks, and outcomes.
- Partner with Technology, Machine Learning, and business teams to integrate semantic technology and support data harmonization.
What they ask for
- Bachelor’s degree in a listed field: information science, computer science, engineering, library science, ontology, semantics, or computational linguistics.
- At least 4 years’ experience in a metadata field such as ontology, taxonomy, semantics, or computational linguistics.
- At least 4 years’ experience or training in W3C standards, including linked and canonical data and ontologies.
- At least 4 years’ experience or training with ontology and linked data tools, and with SQL or SPARQL.
Nice to have
- Master’s degree or PhD in a listed field.
- Familiarity with graph databases and technologies, Python or R, JSON, OpenAPI/YAML, AVRO, Agile, or Amazon Web Services.
Pay
Chicago, IL: $149,800 - $171,000 USD base salary; McLean, VA: $164,800 - $188,100 USD base salary; New York, NY: $179,700 - $205,100 USD base salary; Richmond, VA: $149,800 - $171,000 USD base salary
Tools
JSON, XML, RDF, RDFS, OWL, SKOS, Protégé, SQL
AI focus
Supports integration of semantics with Machine Learning teams and use of machine-learning-capable tools for knowledge organization and semantic enrichment.
Model-written summary · the original posting is authoritative