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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.
- 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.
- 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.
- 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.
- 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.
Skills Required
- Bachelor's degree in computer science, math, statistics, or related field
- Internship experience with data analysis or creation of library data
- Entry-level analytical and communication skills with attention to data quality working under regular supervision
- Foundational knowledge of MARC, Dublin Core, KBART, or BIBFRAME
- Exposure to scripting languages (Python, SQL, or XSLT)
- Familiarity with data transfer protocols (SFTP, APIs, AWS S3)
- Conceptual familiarity with cloud data stores (e.g., Snowflake) and visualization tools (e.g., Power BI, Streamlit)
- Interest in applying AI and linked data concepts (e.g., Schema.org) to library data
