Note: The job is a remote job and is open to candidates in USA. KeyBank is a financial services organization seeking an AI-Ready Context Engineer/Ontologist to design and maintain enterprise information architecture for self-service data understanding and AI-ready data and knowledge usage. The role leads enterprise data models, taxonomies, ontologies, metadata governance, and semantic structures while partnering with business, analytics, data science, and AI engineering teams to support trusted analytics, machine learning, and LLM use cases.
Responsibilities
- Lead the development and maintenance of the enterprise data domain model, taxonomy, and ontologies to ensure shared understanding, semantic consistency, and discoverability of data and knowledge assets
- Design and evolve information and semantic models that make enterprise data AI‑ready, supporting use cases ranging from traditional analytics and BI to applied machine learning and LLM‑based experiences (e.g., search, retrieval‑augmented generation, and copilots)
- Operationalize data models, taxonomies, and semantic structures through the Enterprise Data Catalog (Alation)
- Define and enforce standards for data modeling, taxonomy, nomenclature, and semantic structures to ensure consistency and interoperability across business domains and downstream consumption patterns
- Provide authoritative guidance on semantic conflicts—resolve definition discrepancies, harmonize terms, and mediate cross‑domain dependencies to establish trusted, reusable business meaning
- Contribute to the enterprise data product framework by defining domain boundaries, shared dimensions, and semantic contracts that enable cross‑domain interoperability and AI consumption
- Confirm and document prioritized metadata elements for key business processes, analytical use cases, and AI‑enabled workflows, ensuring alignment with governance standards and risk expectations
- Identify simplification opportunities—reduce redundancy, converge overlapping datasets, and promote canonical sources to improve trust, efficiency, and reusability across analytics and AI platforms
- Partner with analytics, data science, and AI engineering teams to ensure information architecture, metadata, and semantic context are sufficient to support explainable, governed, and trustworthy AI outcomes
- Serve as a thought partner, provide insights from modeling, catalog adoption, and AI enablement to shape governance strategy and roadmaps
Skills
- 10+ years of experience working with data, metadata, and reference data frameworks, including experience in metadata management and/or data quality monitoring
- Experience leading the development of enterprise business glossaries, domain models, and ontologies to enable semantic consistency, shared understanding, and AI ready data usage
- Demonstrated experience with data management concepts including data governance, data quality, master data management, data lineage, and metadata management
- Proven ability to establish and operationalize metadata governance functions, including policies, standards, roles, and controls
- Demonstrated verbal and written communication skills, with strong data, metadata, and governance storytelling that drives adoption and influences stakeholders
- Hands on experience implementing and scaling an Enterprise Data Catalog or metadata repository (Alation or equivalent), including curation workflows and adoption strategies
- Understanding of how semantic models, metadata, and knowledge representation enable applied AI and LLM use cases, such as search, question answering, and decision support
- Strong business acumen in relating data to business process drivers and performance management, with a value delivery mindset
- Collaborative, team focused delivery experience that drives outcomes across enterprise data, analytics, and technology organizations
- Strategic thinker with the ability to translate enterprise objectives into actionable plans and measurable outcomes
- Excellent knowledge of data and metadata management principles, business analysis, and process engineering
- Knowledge Graphs
- Neo4j
- Stardog
- Amazon Neptune / Azure Cosmos DB (Graph)
- Ontology & Semantic Modeling
- OWL / RDF / SKOS
- Protégé
- TopBraid
- Stardog Studio
- Enterprise Data & Knowledge Catalogs
- Alation
- Collibra
- Microsoft Purview
- DataHub
- Knowledge Modeling Techniques
- Ontologies & domain models
- Business vocabularies & taxonomies
- Semantic normalization
- Entity & relationship modeling
- AI Context Delivery (Grounding Layer)
- Vector databases (Pinecone, Weaviate, Azure AI Search)
- Graph + vector retrieval (hybrid RAG)
- Metadata‑driven prompt context
Benefits
- Eligibility for incentive compensation which may include production, commission, and/or discretionary incentives.
- Flexible options in circumstances where roles can be performed effectively in a mobile environment.
Company Overview