Enterprise Information Architect; Taxonomist

Join Aquent Talent partnering with a leading organization in the financial services sector, dedicated to empowering individuals and institutions through innovative solutions and robust information management. This is an opportunity to contribute to a dynamic environment where your expertise will directly shape how knowledge is organized, accessed, and leveraged across the enterprise.

• Design, build, and maintain enterprise taxonomies, controlled vocabularies, and hierarchical classification systems for diverse content and data domains.
• Conduct comprehensive content inventories, gap analyses, and requirements gathering to inform and refine taxonomy structures and scope.
• Define and enforce vocabulary standards, preferred/alternate terms, and relationships aligned with industry best practices.
• Develop and implement metadata schemas and tagging guidelines to ensure consistent content classification across all platforms.
• Train and empower stakeholders and content teams in effective taxonomy application and metadata best practices.
• Engineer and implement ontologies using Semantic Web standards (RDF/RDFS, OWL, SKOS) to precisely model domain knowledge and entity relationships.
• Develop and maintain robust knowledge graphs, integrating taxonomies, ontologies, and instance data to power AI, search, and analytics initiatives.
• Manage and govern ontology change requests, including tooling requirements and process improvements.
• Perform authority control and entity management for critical organizational entities (e.g., people, organizations, products, concepts).
• Apply validation and reasoning standards (e.g., SHACL, SPIN) to ensure the integrity and consistency of our ontological models.
• Define and continuously evolve the organization’s knowledge architecture, encompassing systems and processes for knowledge creation, classification, retrieval, and reuse.
• Collaborate strategically with data management, search, product management, machine learning, and engineering teams to embed standardized vocabularies and semantic models into data consumption experiences.
• Map unstructured data to structured semantic models, integrating seamlessly with data infrastructure and AI/ML pipelines.
• Establish metadata standards, modeling guidelines, and data lineage documentation in partnership with data governance and engineering teams.
• Facilitate knowledge organization workshops to align enterprise taxonomy and data models across various business units.
• Develop and manage comprehensive taxonomy and ontology governance processes, including change management, review cycles, and versioning.
• Conduct regular audits of information architecture to ensure compliance and optimize classification performance.
• Create detailed process documentation, governance standards, and training materials for taxonomy and metadata programs.
• Serve as a cross‑functional Subject Matter Expert, bridging taxonomy design with data engineering, content strategy, and product teams.
• Effectively communicate complex ontology and taxonomy design decisions to both technical and non‑technical audiences, including senior leadership.

• 5–7+ years of dedicated experience in taxonomy design, ontology engineering, knowledge management, or information architecture.
• Demonstrated expertise in designing and governing enterprise‑scale taxonomies and controlled vocabularies.
• Proficiency with Semantic Web standards, including RDF/RDFS, OWL, SKOS, SPARQL, and SHACL.
• Proven experience in developing or contributing to knowledge graphs or ontology‑backed data models.
• Familiarity with key metadata schema standards (e.g., Dublin Core, Schema.org, BIBFRAME, or domain‑specific equivalents).
• Strong background in data modeling methodologies (conceptual, logical, physical; 3NF, Data Vault, XML Schema).
• Experience with industry ontology frameworks relevant to financial services.
• Bachelor’s or Master’s degree in Library & Information Science, Computer Science, Linguistics, Information Systems, or a closely related field, or equivalent professional experience in knowledge engineering or taxonomy management.

• Familiarity with AI‑adjacent knowledge…