Business Analysis Manager - Semantic Data

Position Summary The Knowledge Asset Manager is responsible for the ongoing stewardship, governance, quality, and maintenance of the shared knowledge and data assets that support AI-enabled sourcing and procurement capabilities.

The role ensures taxonomy definitions, classification schemas, metadata structures, standards libraries, semantic models, and evaluation datasets remain accurate, governed, and aligned to evolving business requirements.

Acting as the functional custodian of the AI knowledge foundation, the Knowledge Asset Manager supports multiple AI-enabled capabilities by maintaining the information structures that enable consistent, reliable, and scalable outcomes.

Working closely with business stakeholders, technology teams, and data partners, the role ensures shared knowledge assets remain fit for purpose and continue to support both existing and future AI use cases.

The role is responsible for defining and evolving the semantic architecture that underpins AI-enabled capabilities, ensuring knowledge structures are machine-interpretable, reusable, and consistent across all solutions Key Responsibilities Own and govern shared knowledge and data structures, ensuring consistent definitions, classifications, and standards across all AI-enabled capabilities Design and maintain ontology models, including relationships between key business concepts (e.g., supplier, service type, contract constructs, sourcing workflows) Define and manage taxonomy, classification schemas, metadata structures, and semantic models as the enterprise standard Act as gatekeeper for changes to shared knowledge structures, approving updates to taxonomy, definitions, and classifications Ensure semantic models are reusable across capabilities and not duplicated at the tool or implementation level Prevent duplication or divergence of business logic across tools or implementations Ensure all knowledge structures are machine-interpretable and optimized for AI consumption, not limited to business-readable formats Manage the lifecycle, governance, ownership, and version control of shared knowledge and data assets to ensure long-term sustainability and reuse.

Lead data quality, governance, and standardization activities that improve the consistency, reliability, and scalability of AI-enabled capabilities. Support onboarding of new AI capabilities by defining and maintaining the knowledge assets, classifications, standards, and reference data required for successful deployment.

Required Qualifications Bachelor's degree or equivalent experience. 5+ years of experience in data stewardship, metadata management, knowledge management, information governance, master data management, business analysis, or related disciplines.

Strong understanding of contractual concepts, legal provisions, and commercial terms, with the ability to define, standardize, and govern business interpretations used across AI-enabled capabilities. Strong understanding of how business data is structured, managed, and consumed across operational systems and digital capabilities.

Experience supporting data quality, governance, lifecycle management, or knowledge management initiatives. Demonstrated ability to maintain consistency, accuracy, and governance across complex information assets used by multiple stakeholders and systems. Strong analytical and problem-solving skills with the ability to identify data quality issues, inconsistencies, and opportunities for improvement.

Strong stakeholder management skills and experience working with business, technology, and data teams.

Experience with semantic technologies and standards (e.g., RDF, RDFS, OWL, SKOS, SHACL or similar frameworks) Experience designing or working with ontology models or structured semantic knowledge representations Preferred Qualifications Experience working with Contract Lifecycle Management (CLM) platforms such as Coupa, Legisway, Icertis, Ironclad, Agiloft, or similar solutions.

Experience supporting contract intelligence, contract analytics, knowledge management, workflow automation, or AI-enabled capabilities. Our Interview Practices To maintain a fair and genuine hiring process, we kindly ask that all candidates participate in interviews without the assistance of AI tools or external prompts.

Our interview process is designed to assess your individual skills, experiences, and communication style. We value authenticity and want to ensure we’re getting to know you—not a digital assistant. To help maintain this integrity, we ask to remove virtual backgrounds and include in-person interviews in our hiring process.

Please note that use of AI-generated responses or third-party support during interviews will be grounds for disqualification from the recruitment process. Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.