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# Associate Data/ Metadata Analyst

- **Company:** Oclc Online Computer Library Center, Inc.
- **Location:** Dublin, OH, United States
- **Experience:** Experienced
- **Contract:** Internship / Graduate position
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon S3, Data Analysis, Application Integration Architecture, Cloud Database, Encodings, Data Transmissions, Information Engineering, Data Visualization, Information Lifecycle Management, Python (Programming Language), Linked Data, Metadata, Power BI, SQL Databases, Extensible Stylesheet Language Transformations (XSLT), Data Processing, Scripting, File Transfer Protocol (FTP), Snowflake, Schema.org, Information Technology, Data Management, Streamlit Framework
- **Published:** August 11, 2026
- **Apply:** https://wd1.myworkdaysite.com/recruiting/oclc/OCLC_Careers/job/Dublin-OH--Hybrid/Associate-Data--Metadata-Analyst_R0003901-1

## About the Role

* Bachelor's degree in computer science, math, statistics, or related field and internship experience with data analysis or creation of library data.
 * Entry-level analytical and communication skills, with attention to data quality while working under regular supervision.

## Description

The Associate Data Analyst/Metadata Specialist manages the lifecycle of bibliographic and holdings data, ensuring accuracy and integrity through evaluation, transformation, and maintenance. This entry-level role combines metadata expertise with developing technical skills in scripting, automation, and AI-assisted workflows. Working under regular supervision, the Associate will transition from executing well-defined tasks to taking ownership of complex data projects. This is an opportunity to develop expertise at the intersection of library data, modern data engineering, and emerging AI - in a team that invests in the growth of its people., * Data Lifecycle Management: Perform data ingest, normalization, enrichment, and matching according to established standards (e.g., MARC, KBART).

 * Quality Assessment: Profile incoming data to identify and resolve structural issues, encoding errors, and tagging discrepancies.
 * Troubleshooting: Investigate and resolve data quality issues, escalating complex problems to senior staff as needed.
 * Vendor Communication: Coordinate with external data providers and partner institutions regarding routine data specifications and quality issues., * Pipeline Support: Apply existing scripts and automated workflows to process data; identify inefficiencies and suggest process improvements.
 * Data Visualization: Use and interpret dashboards (e.g., Power BI) to monitor data quality trends and communicate findings.
 * AI Integration: Utilize AI-assisted tools for record matching and field mapping; contribute structured feedback that helps improve AI model accuracy over time., * Technical Tools: Exposure to scripting languages (Python, SQL, or XSLT) and data transfer protocols (SFTP, APIs, or AWS S3).
 * Data Platforms: Conceptual familiarity with cloud data stores (e.g., Snowflake) and visualization tools (e.g., Power BI, Streamlit).
 * Emerging Tech: Interest in applying AI and linked data concepts (e.g., Schema.org) to library data challenges.


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