AI-Ready Knowledge Architect

ESSENTIAL JOB FUNCTIONS

• 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.

REQUIRED EXPERIENCE

• 10+ years of experience working with data, metadata, and reference data frameworks, including experience in metadata management and/or data quality monitoring.
• 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.