Taxonomists bridge human language and machine-readable data structures by creating standardized systems for classifying content. While general document management specialists focus on the operational deployment, security, and lifecycle management of electronic records systems, taxonomists concentrate specifically on semantic modeling, attribute definition, and hierarchical organization. Daily tasks involve analyzing user search behavior, reconciling synonym rings, building category trees, and translating complex subject domains into consistent metadata standards that drive findability.The rhythm of the role balances meticulous analytical work with cross-functional negotiation. Practitioners frequently examine large datasets, resolve naming ambiguities, and establish data governance policies alongside engineers, merchandisers, and content creators. Individuals who thrive in this career typically demonstrate strong critical thinking, systems analysis, and attention to detail, enjoying the challenge of transforming unstructured or messy information into clear, structured architectures.
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Responsibilities
Design and maintain controlled vocabularies, product hierarchies, metadata models, and subject classification frameworks.
Collaborate with engineering, product, and content teams to enhance search relevance, filtering capabilities, and asset discoverability.
Establish and enforce metadata standards, data quality guidelines, and attribution governance policies across enterprise systems.
Analyze catalog data, search queries, and navigation logs to identify gaps in classification structures and taxonomy performance.
Build and maintain semantic models, ontology schemas, or authority files using standards such as RDF, OWL, SKOS, or MARC.
Audit master data, product information management systems, and digital asset repositories to ensure ongoing data integrity and compliance.
Facilitate stakeholder alignment on enterprise terminology, categorization schemes, and syndication requirements.
Qualifications
Bachelor's degree in Library Science, Information Management, Computer Science, or a related field.
Three to five years of professional experience in taxonomy design, metadata management, cataloging, or data governance.
Demonstrated proficiency in configuring classification structures within Product Information Management (PIM), Digital Asset Management (DAM), or Integrated Library Systems (ILS).
Strong technical comprehension of metadata standards, information architecture principles, and controlled vocabulary development.
Proven analytical and systems analysis skills applied to resolving complex cataloging or categorization challenges.
Nice to Have
Master of Library Science (MLS) or Master of Library and Information Science (MLIS) from an ALA-accredited institution.
Professional data governance credentials such as the Certified Data Management Professional (CDMP) or Data Governance and Stewardship Professional (DGSP).
Hands-on experience with semantic web technologies, knowledge graph modeling, and graph database tools such as Neo4j.
Experience applying domain-specific classification standards such as Library of Congress Subject Headings (LCSH) or Resource Description and Access (RDA).
Environment
Primarily office-based work conducted in hybrid or remote settings, with on-site requirements common in physical library or archive collections.
Collaborative interactions with cross-functional technical, legal, marketing, and operational stakeholders.
Standard business hours with occasional project-driven intensity during system migrations or catalog launches.
Extensive use of specialized software including taxonomy management tools, digital asset management platforms, graph databases, and enterprise content management systems.
Benefits & Growth
Competitive base compensation typically supplemented by annual corporate performance bonuses in enterprise and e-commerce sectors.
Clear advancement paths into roles such as Lead Ontologist, Director of Data Governance, or Head of Enterprise Information Architecture.
Support for continuous education, industry conferences, and professional certifications in data governance and semantic technologies.
Opportunities to shape foundational data architectures powering modern artificial intelligence, machine learning, and knowledge graph initiatives.