Product Owner – Ontology

Job Description: Product Owner – Ontology & Knowledge Graph

Domain: Ontology, Semantic Data Management & Knowledge Graphs
Primary Skills: Ontology Modeling, Knowledge Representation, Semantic Technologies, Product Ownership, Data Governance

Job Summary

We are seeking an experienced Product Owner – Ontology to own the vision, strategy, roadmap, and delivery of enterprise ontology and semantic data solutions. The ideal candidate will have strong expertise in ontology design, knowledge representation, semantic modeling, taxonomies, controlled vocabularies, and knowledge graph technologies.

The candidate will be responsible for defining enterprise domain models, establishing ontology governance standards, and translating business requirements into structured semantic models. This role requires close collaboration with business stakeholders, data engineering, enterprise architecture, risk, and compliance teams to enable trusted, governed semantic data capabilities for analytics, knowledge graphs, LLMs, and AI agents.

Key Responsibilities

1. Product Vision & Ownership

• Own the ontology product vision, strategy, roadmap, backlog, and release planning.

• Define product priorities based on business requirements and enterprise data objectives.

• Drive ontology adoption across business domains and technology teams.

• Translate business needs into epics, user stories, acceptance criteria, and measurable product outcomes.

• Manage product releases and ensure successful delivery of ontology capabilities.

2. Ontology Design & Knowledge Representation

• Define and maintain enterprise ontology models and semantic standards.

• Design class hierarchies, entity relationships, cardinality, inheritance, and constraints.

• Develop taxonomies, controlled vocabularies, and standardized domain definitions.

• Establish reusable semantic models to ensure consistency across enterprise systems.

• Define competency questions to validate ontology coverage and business relevance.

3. Semantic Technologies & Knowledge Graphs

• Work with semantic web and ontology technologies, including:

  • RDF (Resource Description Framework)

  • OWL (Web Ontology Language)

  • SKOS

  • SHACL

  • SPARQL

  • Neo4j / Cypher or comparable knowledge graph technologies

• Support the development and validation of knowledge graphs.

• Enable semantic relationships and knowledge representation across enterprise data.

• Support semantic grounding for analytics, LLMs, and AI agents.

4. Ontology Governance & Data Quality

• Establish ontology governance, stewardship, and ownership frameworks.

• Define versioning, change control, and impact assessment processes.

• Maintain data lineage, quality metrics, permissions, and auditability.

• Establish standards for ontology validation and semantic consistency.

• Monitor ontology quality and drive continuous improvements.

5. Business & Data Integration

• Partner with business subject matter experts, data engineers, enterprise architects, risk, and compliance teams.

• Map source-system data to enterprise ontology models.

• Perform source-data profiling, data mapping, and semantic model validation.

• Identify data gaps, inconsistencies, dependencies, and integration requirements.

• Ensure ontology models accurately represent business concepts and relationships.

6. Requirements & Delivery Management

• Convert business requirements into epics, user stories, competency questions, and acceptance criteria.

• Coordinate with technical teams to translate ontology requirements into implementation deliverables.

• Define production validation tests to verify semantic models and ontology releases.

• Track backlog progress, dependencies, risks, and release milestones.

• Ensure delivered solutions meet functional, technical, and governance requirements.

7. AI & Enterprise Data Enablement

• Enable governed semantic grounding for knowledge graphs, analytics, LLMs, and AI agents.

• Ensure semantic models support accurate and consistent retrieval of enterprise knowledge.

• Define appropriate access permissions, governance controls, and auditability for AI-driven use cases.

• Collaborate with AI and data engineering teams to support enterprise knowledge representation.

Required Technical & Functional Skills

Ontology & Knowledge Representation

• Strong understanding of ontology design and knowledge representation.

• Experience with class hierarchies, relationships, cardinality, inheritance, and constraints.

• Knowledge of taxonomies and controlled vocabularies.

• Ability to define and maintain enterprise semantic models.

Semantic Technologies

• RDF

• OWL

• SKOS

• SHACL

• SPARQL

• Neo4j / Cypher or equivalent semantic and knowledge graph technologies.

Data Management & Validation

• Strong SQL knowledge.

• Source-data profiling and analysis.

• Data mapping and transformation.

• Semantic model validation.

• Data lineage and quality management.

Product Ownership & Governance

• Product vision, roadmap, backlog, and release management.

• Requirements gathering and stakeholder management.

• Epics, user stories, competency questions, and acceptance criteria.

• Ontology governance, stewardship, versioning, and change control.

• Impact assessment, permissions, and auditability.

Preferred Qualifications

• Experience in the mortgage or financial services domain.

• Knowledge of MISMO (Mortgage Industry Standards Maintenance Organization) standards.

• Experience developing mortgage-related ontologies and semantic data models.

• Exposure to enterprise knowledge graphs and AI/LLM-based applications.

• Experience working with cross-functional global teams.