Information Architect

Overview

Join a leading organization in the financial services industry, where innovation meets impact. This company is dedicated to empowering its customers through robust information architecture and cutting‑edge data solutions, driving clarity and efficiency in a complex financial landscape. Aquent Talent is seeking a highly skilled individual to join this forward‑thinking team.

Are you a visionary in knowledge organization, eager to shape how critical information is structured, accessed, and leveraged? We are seeking a dynamic individual to play a pivotal role in designing and evolving the very fabric of our enterprise knowledge. In this position, you will be instrumental in bridging the gap between data, content, and AI, directly influencing how our organization makes informed decisions and delivers exceptional value to our customers.

Your expertise will be key to building sophisticated knowledge systems that power advanced analytics, intelligent search, and next‑generation AI applications.

• Design, build, and maintain enterprise taxonomies, controlled vocabularies, and hierarchical classification systems across content and data domains.
• Conduct content inventories, gap analyses, and requirements gathering to inform taxonomy structure and scope.
• Define vocabulary standards, preferred/alternate terms, broader/narrower relationships, and cross‑references aligned with industry standards (ANSI/NISO Z39.19, ISO 25964).
• Develop and enforce metadata schemas and tagging guidelines to support consistent content classification across platforms.
• Train stakeholders and content teams in taxonomy application and metadata best practices.
• Design and implement ontologies using Semantic Web standards (RDF/RDFS, OWL, SKOS) to model domain knowledge and entity relationships.
• Develop and maintain knowledge graphs that integrate taxonomies, ontologies, and instance data to support AI, search, and analytics use cases.
• Create, validate, and govern ontology change requests; own ontology tooling requirements and process improvements.
• Perform authority control and entity management for organizational entities (people, organizations, products, concepts).
• Apply validation and reasoning standards (SHACL, SPIN) to ensure ontology integrity and consistency.
• Define and evolve the organization’s knowledge architecture — the structures, systems, and processes that enable knowledge creation, classification, retrieval, and reuse.
• Partner with data management, search, product management, ML, and engineering teams to implement standardized vocabularies and semantic models in data consumption experiences.
• Map unstructured data to structured semantic models that integrate with data infrastructure and AI/ML pipelines.
• Establish metadata standards, modeling guidelines, and data lineage documentation in collaboration with data governance and engineering teams.
• Support knowledge organization workshops to align enterprise taxonomy and data models across business units.
• Develop and manage taxonomy and ontology governance processes, including change management, review cycles, and versioning.
• Perform regular audits of information architecture to maintain compliance and optimize classification performance.
• Write 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.
• Communicate ontology and taxonomy design decisions to both technical and non‑technical audiences, including senior leadership.

• 5-7+ years of experience in taxonomy design, ontology engineering, knowledge management, or information architecture.
• Demonstrated experience designing and governing enterprise‑scale taxonomies and controlled vocabularies.
• Proficiency with Semantic Web standards: RDF/RDFS, OWL, SKOS, SPARQL, SHACL.
• Experience developing or contributing to knowledge graphs or ontology‑backed data models.
• Familiarity with metadata schema standards (e.g., Dublin Core, Schema.org, BIBFRAME, or domain equivalents).
• Background in data modeling methodologies (conceptual,…