Senior Semantic Data Architect

SUMMARY This position is responsible for anchoring the design, development, and governance of the enterprise semantic model that underpins the Insperity AI Platform. It defines how business entities, relationships, and rules are consistently represented across domains, enabling reliable AI, analytics, and data products. The role partners with subject matter experts, AI Program Managers, Platform Architecture, and governance bodies to translate business definitions into a unified semantic layer, ensuring ongoing alignment, adoption, and integrity of the model as business processes and platform capabilities evolve. RESPONSIBILITIES Designs and maintains the enterprise semantic model (ontology, taxonomy, and controlled vocabularies) across HR, Payroll, Benefits, Risk, and Service Operations domains, ensuring consistent representation of core business entities and relationships across the Insperity AI portfolio. Elicits authoritative business definitions from subject matter experts and resolves cross-domain definitional conflicts, codifying the result into a unified semantic layer used by AI agents, analytics, and downstream data products. Defines and maintains data product standards covering schema conventions, naming, definitional consistency, and lineage; partners with engineering and data leadership to drive adoption across business units and fusion teams. Partners with Platform Architects and Enterprise Data Engineering to integrate the semantic model into the Insperity AI Platform's grounding layer, ensuring agents draw on consistent business definitions at inference time and that semantic changes propagate cleanly into production. Partners with AI Program Managers across business units to translate domain definitions into the unified ontology and to keep the model aligned as business processes evolve. Receives knowledge transfer from external semantic layer consultants and assumes long-term ownership of the model post-handoff, ensuring continuity, ongoing evolution, and institutional capability beyond the initial build. Governs the change control process for the semantic model, reviewing additions, deprecations, and modifications against impact on downstream consumers; partners with engineering and data leadership on release coordination. Documents the semantic model in forms usable by both technical (engineering, data) and business (operations, compliance) audiences, including conceptual diagrams, definitional references, and lineage views. Partners with Legal, Compliance, and the AI Governance Board to align definitional decisions with policy, regulatory requirements, and responsible AI principles, particularly where semantic choices intersect with regulated data domains. Maintains traceability between business definitions, data product schemas, and downstream consumers (AI agents, analytics, reporting), enabling impact analysis and confident change management as the platform scales. Champions definitional rigor across the AI Platform program, building shared understanding of why consistent business representation is foundational to trustworthy AI and engaging stakeholders to reinforce that discipline. QUALIFICATIONS Bachelor’s Degree or higher in Information Science, Computer Science, Library Science, Knowledge Management, Linguistics, or related field. Five to seven years of proven experience in semantic modeling, ontology engineering, taxonomy design, or enterprise data architecture. Three years of experience working with the data foundations of AI applications, including semantic layers, knowledge graphs, or retrieval-augmented generation grounding. Demonstrated experience receiving and operationalizing knowledge transfer from an external consulting engagement. Demonstrated experience designing and maintaining a semantic model consumed by multiple downstream systems, including AI applications, analytics platforms, and operational data products. Working knowledge of at least one formal semantic modeling approach, such as OWL/RDF, Object Role Modeling, UML conceptual modeling, or relational semantic layer platforms (e.g., Cube, dbt, AtScale). Strong skill at eliciting definitions from non-technical subject matter experts and translating them into precise, machine‑usable models. Proven ability to facilitate cross‑functional definitional disputes to consensus, including where stakeholders disagree on whether a term means the same thing across business contexts. Excellent communication, collaboration, and problem‑solving skills, with the ability to translate between technical and business audiences. Ability to think strategically and creatively and adapt to changing needs and priorities. Proficiency in documentation tools and modeling environments (e.g., Protege, PoolParty, ontology IDEs, or equivalent semantic‑layer authoring tools). Demonstrated leadership in data foundations for AI, including data governance, cataloging, lineage, quality standards, metadata management, and labeling workflows. Familiarity with graph databases (e.g., Neo4j, GraphDB, Amazon Neptune) or knowledge graph platforms. Familiarity with data governance frameworks (e.g., DAMA‑DMBOK, DCAM) and data product practices. Strong proficiency in retrieval-based AI grounding, including hybrid RAG, citations, and indexing/chunking strategies as they intersect with the semantic layer. Domain experience in HR, Payroll, Benefits, or Professional Employer Organization (PEO) services is preferred. Working knowledge of security, compliance, and responsible AI principles, including PII handling, data minimization, and lineage requirements for regulated domains. At Insperity, we celebrate the diversity of our employees and our leadership. Insperity is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status or any other characteristic protected by law. #J-18808-Ljbffr 60 Insperity Services, L.P.