Standard Job Description
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The Data and AI Enablement organization is seeking an Enterprise Ontology Product Owner to serve as the connective role between the business functions that own enterprise ontologies and the technical teams that build them. This person ensures that ontology models are grounded in real business meaning, validated by the people who use them, and adopted across AI-enabled experiences, analytics tools, and data products.
This is not a technical modeling role. There are dedicated technical teams responsible for building and maintaining ontologies. Instead, this role requires someone who can operate fluently across business functions, translate business language and logic into requirements the technical team can act on, and hold the line on quality, consistency, and adoption. Success depends on deep functional domain expertise, strong facilitation skills, and enough technical fluency to be a credible partner to data engineering and AI/ML teams, not on the ability to build ontologies directly.
What You’ll Do
- •Validate and prioritize ontology use cases – ensure models align with business goals, governance policies, and real‑world terminology while coaching business functions to develop competency questions that test scope and correctness.
- •Translate business intent into clear, engineer‑ready requirements and bring stakeholders in as co‑creators through workshops, so data‑engineering and AI/ML teams can build efficiently and without duplication.
- •Collaborate with data product owners and platform teams to guarantee that ontology‑driven data products integrate smoothly with the broader data‑product ecosystem, avoiding conflicts or redundant effort.
- •Drive delivery and quality assurance – track progress, remove blockers, enforce production‑grade data‑pipeline controls, and evaluate the quality and business value of engineered outputs before stakeholder hand‑off.
- •Communicate value and promote adoption – explain modeling decisions in plain language, showcase possibilities with generative‑AI tools, and champion the product to senior leadership and end users.
- •Maintain a continuous feedback loop – gather ongoing stakeholder input, iterate on ontology scope, and update success metrics (adoption, usage, consistency, AI answer quality, business impact) to demonstrate measurable value.
- •Lead cross‑functional delivery teams and partner with business function owners to clarify RACI responsibilities while owning the overall process, coordination, and quality of the ontology.
Basic Qualifications
- •Experience working with or on ontologies, knowledge graphs, or semantic data models in a professional capacity.
- •Proven ability to partner with business stakeholders to translate requirements into structured documentation or models.
- •Strong written and verbal communication skills, with a track record of explaining technical concepts to non‑technical audiences.
What Success Looks Like
You’ll know you are successful when business functions trust the ontology as an accurate reflection of how they define their own data and processes, technical teams receive clear, validated requirements to build against, and adoption of ontology‑driven capabilities grows measurably across AI‑enabled experiences, analytics tools, and data products. Success is demonstrated by high usage metrics, concrete business impact, and a continuous cycle of stakeholder‑driven evolution of the product.
Must be a U.S. citizen.
Basic Qualifications
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- •Experience working with or on ontologies, knowledge graphs, or semantic data models in some professional capacity
- •Experience partnering with business stakeholders to translate business requirements into structured documentation or models
- •Strong written and verbal communication skills, with demonstrated ability to explain technical or structured concepts to non-technical audiences
Desired Skills
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- •Deep functional domain expertise in one or more enterprise areas (e.g., finance, supply chain, sales, HR, product).
- •Background in product ownership, business analysis, or a coordination‑heavy role focused on validation of use cases.
- •Experience with enterprise data products, platforms, and pipelines (Databricks, data warehouses, MDM, etc.).
- •Experience with data governance, metadata management, or master‑data programs.
- •Familiarity with AI‑enabled experiences, analytics tools, and the broader data‑product ecosystem.
- •Ability to facilitate workshops and engage stakeholders as co‑creators.
- •Understanding of how ontologies improve AI answer quality, semantic search/discovery, and overall business value.
- •Experience with large‑scale enterprise systems (SAP, ERP, etc.) to anchor business terminology in operational data.
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