Position Summary
The Associate Data Integration Analyst is responsible for maintaining, enriching, and improving the quality, accuracy, and completeness of bibliographic and library catalog data across the organization's products and services. This role serves as a critical link between data management, reporting, software development, and AI-enabled metadata enrichment initiatives.
The ideal candidate is highly analytical, detail-oriented, and passionate about data quality. They will work closely with software developers, product stakeholders, operational teams, and AI initiatives to identify and resolve data issues, improve catalog integrity, support business reporting needs, and contribute to the ongoing advancement of AI-driven cataloging and metadata management solutions.
Key Responsibilities
Catalog Data Management and Enrichment
Analyze, validate, maintain, and improve large-scale library catalog and metadata repositories.
Enrich bibliographic records using AI-assisted workflows, automated processes, and data transformation techniques.
Design, execute, and support ETL processes that ingest, normalize, validate, and enhance catalog data from multiple internal and external sources.
Ensure metadata quality, consistency, and compliance with established library standards and best practices.
Monitor catalog integrity and proactively identify opportunities to improve record completeness, discoverability, and overall user experience.
Data Quality and Troubleshooting
Investigate, diagnose, and resolve inaccurate, incomplete, duplicated, or inconsistent catalog and metadata records.
Perform root cause analysis on recurring data issues and develop recommendations for long-term resolution.
Create and maintain data validation, auditing, and quality assurance processes that reduce future errors.
Collaborate with operational and customer support teams to address customer-reported data discrepancies and concerns.
Establish and maintain data quality metrics and monitoring practices.
Reporting and Analytics
Develop and deliver ad hoc reports, dashboards, and data analyses that support business, product, and operational decision-making.
Analyze catalog growth, metadata quality trends, and data performance metrics.
Present findings, recommendations, and actionable insights to leadership and key stakeholders.
Support a data-driven culture by providing meaningful analysis that improves operational effectiveness and product quality.
Software Development Collaboration
Partner closely with software development teams to identify and troubleshoot application defects impacting catalog data accuracy and integrity.
Document data-related defects, provide detailed analysis, and assist with issue prioritization and resolution efforts.
Participate in testing, validation, and quality assurance activities for system enhancements and bug fixes.
Help define requirements and best practices for data quality monitoring, validation tools, and automated controls.
AI Training and Optimization
Support the training, evaluation, and continuous improvement of AI agents used for catalog maintenance, metadata enrichment, and related business processes.
Review AI-generated outputs for accuracy, consistency, completeness, and adherence to business rules.
Assist in building training datasets, testing methodologies, prompt strategies, and feedback mechanisms.
Document AI performance issues and collaborate with development teams to improve AI workflows, outputs, and overall effectiveness.
Required QualificationsBachelor's degree in Data Analytics, Information Science, Library Science, Computer Science, Information Systems, or a related field, or equivalent professional experience.
Strong SQL skills and hands-on experience querying and analyzing relational databases.
Advanced proficiency with Microsoft Excel and reporting tools.
Strong analytical, troubleshooting, and problem-solving skills.
Excellent written and verbal communication abilities.
Ability to effectively collaborate with both technical and non-technical stakeholders.
Strong organizational skills and exceptional attention to detail.
Demonstrated commitment to data accuracy, quality, and continuous improvement.
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.
Knowledge of library systems, bibliographic metadata, MARC records, authority records, or related library standards.
Familiarity with artificial intelligence, machine learning tools, prompt engineering, or AI training workflows.
Experience with Python, scripting languages, or data transformation tools.
Knowledge of data governance, data stewardship, and data quality frameworks.
Experience with business intelligence and visualization tools such as Power BI, Tableau, or similar platforms.
Understanding of API integrations and common data exchange formats including JSON, XML, and CSV.
Core Skills and Competencies
Data Analysis
Data Quality Management
SQL and Relational Databases
ETL Development and Data Transformation
Metadata and Bibliographic Data Management
Library Systems Knowledge
Reporting and Analytics
Dashboard Development
Data Visualization
Root Cause Analysis
Problem Solving
Microsoft Excel
AI Training and Evaluation
AI-Assisted Data Enrichment
Data Validation and Quality Assurance
Business Intelligence Tools (Power BI, Tableau)
API Integration
JSON, XML, CSV
Cross-Functional Collaboration
Technical Documentation
Attention to Detail
Continuous Improvement
Why Join Us
This is an exciting opportunity to play a key role in improving data quality and discoverability across a growing library technology environment. You'll collabor
