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The job details are as follows:
The Associate Data Analyst/Metadata Analyst 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.
Responsibilities:
Data Operations & Quality

• 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.

Technical Solutions & Automation

• 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.

Collaboration & Documentation

• Knowledge Management: Document data source profiles, processing decisions, and technical workflows to ensure team-wide knowledge sharing.
• Team Support: Execute foundational tasks to support senior analysts and participate in platform improvement projects.

Minimum Required Knowledge, Skills and Experience:

• 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.

Preferred Knowledge, Skills and Experience:

• Metadata Standards: Foundational knowledge of MARC, Dublin Core, KBART, or BIBFRAME.
• 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.

Working Conditions: Normal office environment.
ADA/EAA: The above statements cover what are generally believed to be principal and essential functions of this job. Specific circumstances may allow or require some people assigned to the job to perform a somewhat different combination of duties.