AI-Native Metadata Librarian

Third Iron

AI-native metadata librarian improving bibliographic data for Third Iron’s library software. Analyzing datasets, refining metadata workflows, and applying AI-assisted quality control.

Posted 8/6/2026full-timeRemote • 🇺🇸 United StatesJuniorMid-LevelWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in bibliographic metadata analysis, data quality assessment, and the application of AI tools for data transformation and workflow development. Proficient in collaborating with technical teams to enhance metadata architecture and ensure data accuracy and consistency.

Highest-signal resume keywords

Bibliographic Metadata AnalysisData Quality AssessmentExcel Data AnalysisSQL Query ExperienceAI Tools Utilization

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data CleaningData ReconciliationMetadata NormalizationEntity ResolutionData TransformationAPIsJSONXMLRegular ExpressionsScripting

Soft Skills

Analytical SkillsWritten CommunicationRemote CollaborationProblem-SolvingIndependence

Tools & Technologies

AI ToolsData-Analysis MethodsWorkflow DevelopmentMetadata StandardsDiscovery Services

Certifications & Qualifications

Master's Degree in Library and Information Science

Industry Keywords

Bibliographic DataKnowledge BasesScholarly CommunicationLibrary SystemsMetadata Standards

Tech Stack

Tools & technologies

SQL

About the role

Key responsibilities & impact

Analyze, clean, reconcile, and enrich large-scale bibliographic and holdings datasets
Investigate metadata quality, duplication, normalization, matching, and entity-resolution issues
Use Excel, SQL, AI tools, and other data-analysis methods to identify patterns, exceptions, and improvement opportunities
Design and refine metadata rules, mappings, validation processes, and automated workflows
Evaluate bibliographic data from publishers, aggregators, libraries, and other external sources
Support the evolution of metadata architecture, discovery capabilities, and knowledge-base systems
Develop repeatable methods for assessing metadata completeness, accuracy, consistency, and timeliness
Document data sources, standards, workflows, decisions, and quality-control procedures
Use AI-assisted approaches while validating results and maintaining standards for accuracy, privacy, and responsible use
Collaborate with technical and product teams to strengthen metadata quality, coverage, matching, and interoperability
Improve bibliographic data accuracy, consistency, and coverage
Develop scalable approaches to data analysis and quality control
Turn complex bibliographic data into more reliable experiences for libraries and their users

Requirements

What you’ll need

At least 2 years of professional work experience in libraries, data science, metadata analysis, or a similar role
Master's degree in library and information science, a related field, or equivalent professional experience
Professional experience with bibliographic metadata, library systems, discovery services, knowledge bases, or scholarly information
Strong working knowledge of Excel, including functions, filters, lookups, data transformation, and analysis of large datasets
High degree of comfort using AI tools for research, analysis, vibe coding mini apps, workflow development, and problem-solving
Familiarity with bibliographic identifiers and metadata standards such as DOI, ISSN, ISBN, MARC, KBART, and Dublin Core
Strong analytical skills and persistence investigating ambiguous or incomplete data
Ability to explain metadata and data-quality issues to technical and nontechnical colleagues
Excellent written communication, documentation, and remote-collaboration skills
Experience with APIs, JSON, XML, regular expressions, scripting, or data-transformation tools
Comfort reviewing metadata samples and designing tools to analyze data mechanically with AI
Ability to work independently, manage priorities, and contribute within a distributed team
Preferred: SQL query experience
Preferred: familiarity with publisher, aggregator, link-resolver, or library-vendor metadata
Preferred: entity resolution, record matching, deduplication, authority control, or identifier reconciliation
Preferred: scholarly communication data sources such as Crossref, PubMed, OpenAlex, ORCID, or library knowledge bases
Preferred: AI-assisted metadata workflow experience
Preferred: collaboration with software engineers, product managers, or data teams
Preferred: 1+ year of successful remote work
Ability to formulate and refine effective AI prompts and apply human judgment and verification

Benefits

Comp & perks

Health
Dental
Long-term disability
Paid time-off
Home-office stipend
Remote-first, fully distributed work environment