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Asset Metadata Taxonomy Jobs in Ohio (NOW HIRING)

Asset Metadata Taxonomy information

What is an asset metadata taxonomy?

An Asset Metadata Taxonomy is a structured classification system used to organize and categorize digital assets, such as images, videos, documents, and other media, based on their descriptive metadata. This taxonomy helps organizations standardize the way they tag and retrieve assets, making it easier to search for and manage content efficiently. By defining categories, attributes, and relationships, an asset metadata taxonomy ensures consistency, improves discoverability, and supports effective digital asset management across teams and platforms.

What skills and qualifications are needed to thrive as an asset metadata taxonomy specialist?

To excel as an Asset Metadata Taxonomy Specialist, you need a strong background in information science, metadata standards, taxonomy development, and data organization, often supported by a degree in library science or a related field. Familiarity with content management systems (CMS), digital asset management (DAM) platforms, and metadata schema tools is essential. Attention to detail, analytical thinking, and effective communication are critical soft skills for collaborating with stakeholders and ensuring consistent taxonomy application. These skills ensure accurate asset classification, improve content discoverability, and support efficient information retrieval across digital platforms.

What are common challenges faced by professionals working in asset metadata taxonomy roles, and how can they be addressed?

Professionals in Asset Metadata Taxonomy often encounter challenges such as ensuring consistency across large and evolving datasets, integrating disparate metadata standards, and coordinating with multiple stakeholders like IT, content creators, and business units. Addressing these challenges typically involves establishing clear governance frameworks, utilizing robust taxonomy management tools, and maintaining open communication with cross-functional teams. Staying updated on industry best practices and regularly reviewing the taxonomy structure can also help prevent data silos and improve overall asset discoverability.

What is the difference between Asset Metadata Taxonomy vs Asset Data Analyst?

AspectAsset Metadata TaxonomyAsset Data Analyst
Primary FocusOrganizing and classifying asset metadataAnalyzing asset data for insights and reporting
Skills RequiredTaxonomy development, metadata standards, data managementData analysis, statistical skills, reporting tools
Work EnvironmentData management teams, information systemsBusiness units, analytics teams
CertificationsData management, taxonomy certificationData analysis, business intelligence certifications

While both roles involve working with asset data, Asset Metadata Taxonomy focuses on structuring and categorizing metadata for assets, whereas Asset Data Analysts interpret and analyze asset data to support decision-making. Understanding these differences helps organizations assign the right skills to each role.

What are popular job titles related to Asset Metadata Taxonomy jobs in Ohio?

For Asset Metadata Taxonomy jobs in Ohio, the most frequently searched job titles are:

What job categories do people searching Asset Metadata Taxonomy jobs in Ohio look for?

The top searched job categories for Asset Metadata Taxonomy jobs in Ohio are:

What cities in Ohio are hiring for Asset Metadata Taxonomy jobs?

Cities in Ohio with the most Asset Metadata Taxonomy job openings:

AI-Ready Context Engineer/ Ontologist

Keybank

Brooklyn, OH

Full-time

Posted 3 days ago

New


KeyBank rating

8.2

Company rating: 8.2 out of 10

Based on 99 frontline employees who took The Breakroom Quiz

51st of 171 rated banks


Job description

Location:

4910 Tiedeman Road, Brooklyn Ohio

JOB DESCRIPTION:

The AI-Ready Context Engineer/ Ontologist plays a critical role in designing and maintaining the enterprise information architecture essential for cataloging KeyBank's data for selfservice understanding and enabling AIready data and knowledge usage. This role defines and enforces standards for data modeling, taxonomy, semantic structures, and knowledge representation to ensure consistency, interoperability, and clarity across the organization.

The AI-Ready Context Engineer/ Ontologist partners closely with business and technology teams to develop and maintain the enterprise data domain model and ontologies that support governance frameworks, trusted analytics, and downstream consumption across business intelligence (BI), applied AI/ML, and Large Language Model (LLM) use cases. Success in this role requires the ability to translate complex theoretical concepts into scalable, governed information structures that drive adoption of the data catalog, support emerging AI capabilities, and deliver measurable value to colleagues.

ESSENTIAL JOB FUNCTIONS:

  • Lead the development and maintenance of the enterprise data domain model, taxonomy, and ontologies to ensure shared understanding, semantic consistency, and discoverability of data and knowledge assets.
  • Design and evolve information and semantic models that make enterprise data AIready, supporting use cases ranging from traditional analytics and BI to applied machine learning and LLMbased experiences (e.g., search, retrievalaugmented generation, and copilots).
  • Operationalize data models, taxonomies, and semantic structures through the Enterprise Data Catalog (Alation).
  • Define and enforce standards for data modeling, taxonomy, nomenclature, and semantic structures to ensure consistency and interoperability across business domains and downstream consumption patterns.
  • Provide authoritative guidance on semantic conflicts-resolve definition discrepancies, harmonize terms, and mediate crossdomain dependencies to establish trusted, reusable business meaning.
  • Contribute to the enterprise data product framework by defining domain boundaries, shared dimensions, and semantic contracts that enable crossdomain interoperability and AI consumption.
  • Confirm and document prioritized metadata elements for key business processes, analytical use cases, and AIenabled workflows, ensuring alignment with governance standards and risk expectations.
  • Identify simplification opportunities-reduce redundancy, converge overlapping datasets, and promote canonical sources to improve trust, efficiency, and reusability across analytics and AI platforms.
  • Partner with analytics, data science, and AI engineering teams to ensure information architecture, metadata, and semantic context are sufficient to support explainable, governed, and trustworthy AI outcomes.
  • Serve as a thought partner, provide insights from modeling, catalog adoption, and AI enablement to shape governance strategy and roadmaps.

REQUIRED EXPERIENCE:

  • 10+ years of experience working with data, metadata, and reference data frameworks, including experience in metadata management and/or data quality monitoring
  • Experience leading the development of enterprise business glossaries, domain models, and ontologies to enable semantic consistency, shared understanding, and AI ready data usage.
  • Demonstrated experience with data management concepts including data governance, data quality, master data management, data lineage, and metadata management.
  • Proven ability to establish and operationalize metadata governance functions, including policies, standards, roles, and controls.
  • Demonstrated verbal and written communication skills, with strong data, metadata, and governance storytelling that drives adoption and influences stakeholders.
  • Hands on experience implementing and scaling an Enterprise Data Catalog or metadata repository (Alation or equivalent), including curation workflows and adoption strategies.
  • Understanding of how semantic models, metadata, and knowledge representation enable applied AI and LLM use cases, such as search, question answering, and decision support.
  • Strong business acumen in relating data to business process drivers and performance management, with a value delivery mindset.
  • Collaborative, team focused delivery experience that drives outcomes across enterprise data, analytics, and technology organizations.
  • Strategic thinker with the ability to translate enterprise objectives into actionable plans and measurable outcomes.
  • Excellent knowledge of data and metadata management principles, business analysis, and process engineering.

TECHNOLOGIES:

Knowledge Graphs

Neo4j

Stardog

Amazon Neptune / Azure Cosmos DB (Graph)

Ontology & Semantic Modeling

OWL / RDF / SKOS

Protege

TopBraid

Stardog Studio

Enterprise Data & Knowledge Catalogs

Alation

Collibra

Microsoft Purview

DataHub

Knowledge Modeling Techniques

Ontologies & domain models

Business vocabularies & taxonomies

Semantic normalization

Entity & relationship modeling

AI Context Delivery (Grounding Layer)

Vector databases (Pinecone, Weaviate, Azure AI Search)

Graph + vector retrieval (hybrid RAG)

Metadatadriven prompt context

COMPENSATION AND BENEFITS

This position is eligible to earn a base salary in the range of $96,000.00 - $181,000.00 annually. Placement within the pay range may differ based upon various factors, including but not limited to skills, experience and geographic location. Compensation for this role also includes eligibility for incentive compensation which may include production, commission, and/or discretionary incentives.

Please click here for a list of benefits for which this position is eligible.

Key has implemented an approach to employee workspaces which prioritizes in-office presence, while providing flexible options in circumstances where roles can be performed effectively in a mobile environment.

Job Posting Expiration Date: 09/28/2026 KeyCorp is an Equal Opportunity Employer committed to sustaining an inclusive culture. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, genetic information, pregnancy, disability, veteran status or any other characteristic protected by law.

Qualified individuals with disabilities or disabled veterans who are unable or limited in their ability to apply on this site may request reasonable accommodations by emailing HR_Compliance@keybank.com.

#LI-Remote

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About KeyBank

Sourced by ZipRecruiter

Key is one of the nation's largest bank-based financial services companies. Key provides deposit, lending, cash management, insurance, and investment services to individuals and businesses in 15 states under the name KeyBank National Association through a network of more than 1,200 branches and more than 1,500 ATMs. Key also provides a broad range of sophisticated corporate and investment banking products, such as merger and acquisition advice, public and private debt and equity, syndications, and derivatives to middle market companies in selected industries throughout the United States under the KeyBanc Capital Markets trade name.

Industry

Banking and credit intermediation

Company size

10,000+ Employees

Headquarters location

Cleveland, OH, US

Year founded

1849