Associate Data Integration Analyst - Library Services

Associate Data Integration Analyst - Library Services

Position Summary

The Associate Data Integration Analyst is responsible for maintaining, enriching, and improving the quality of bibliographic and library catalog data across the organization's products and services. This role combines data analysis, ETL processes, AI-assisted metadata enrichment, reporting, and data quality management to ensure accurate and comprehensive library records.

The ideal candidate will work closely with the software development team and product stakeholders to identify data issues, improve catalog integrity, support operational reporting, and help train and evaluate AI agents that assist with cataloging and metadata management.

Note: Applicants must be legally authorized to work in the United States; sponsorship is not available.
 

Catalog Data Management & Enrichment

• Analyze, validate, and maintain large-scale library catalog and metadata repositories.
• Enrich bibliographic records using AI-assisted workflows and data transformation processes.
• Design and execute ETL processes to ingest, normalize, and enhance catalog data from multiple sources.
• Ensure metadata quality, consistency, and adherence to library standards and best practices.
• Monitor data integrity and proactively identify opportunities to improve catalog completeness and discoverability.

Data Quality & Troubleshooting

• Investigate, diagnose, and correct inaccurate, duplicate, incomplete, or inconsistent catalog data.
• Perform root-cause analysis on recurring data quality issues.
• Develop data validation and quality assurance processes to prevent future errors.
• Collaborate with operational teams to resolve customer-reported data issues.

Reporting & Analytics

• Create and deliver ad hoc reports, dashboards, and analyses to support business and operational needs.
• Analyze catalog growth, data quality trends, and system performance metrics.
• Present findings and recommendations to management and stakeholders.
• Support data-driven decision-making through actionable insights.

Software Development Collaboration

• Partner with the software development team to identify application defects that may result in data inaccuracies.
• Document data-related bugs and provide detailed analysis to support troubleshooting and remediation efforts.
• Participate in system testing and validation of fixes impacting catalog and metadata quality.
• Help define requirements for data quality monitoring and validation tools.

AI Training & Enablement

• Support the training and improvement of AI agents used for metadata enrichment, catalog maintenance, and related business functions.
• Evaluate AI-generated outputs for accuracy, consistency, and compliance with business rules.
• Assist in developing training datasets, testing procedures, and feedback mechanisms for AI systems.
• Document AI performance issues and collaborate with the software development team to improve results and workflows.

Required Qualifications

• Bachelor's degree in Data Analytics, Information Science, Library Science, Computer Science, Information Systems, or a related field (or equivalent experience).
• Strong SQL skills and experience querying relational databases.
• Proficiency with Microsoft Excel and reporting tools.
• Strong analytical, troubleshooting, and problem-solving skills.
• Excellent written and verbal communication skills.
• Ability to work effectively with both technical and non-technical stakeholders.
• Strong attention to detail and commitment to data accuracy.
   

Preferred Qualifications

• 1-3 years of experience in data analysis, data quality management, library systems, or a related analytical role.
• Experience working with large datasets and ETL processes.
• Experience working with library systems, bibliographic metadata, MARC records, authority records, or related library standards.
• Familiarity with AI/ML tools, prompt engineering, or AI training workflows.
• Experience with Python, data transformation tools, or scripting languages.
• Knowledge of data governance and data quality frameworks.
• Experience with business intelligence tools such as Power BI, Tableau, or similar platforms.
• Understanding of API integrations and data exchange formats (JSON, XML, CSV).
   

Key Competencies

• Data Analysis
• Data Quality Management
• ETL Development
• Metadata Management
• Problem Solving
• Root Cause Analysis
• Reporting & Visualization
• Cross-Functional Collaboration
• AI Training & Evaluation
• Attention to Detail
• Continuous Improvement