Emory-posted 3 days ago
Full-time • Entry Level
Onsite • Atlanta, GA
5,001-10,000 employees

Reporting to the Head of Assessment, the Data Analyst supports data-informed decision-making across Emory Libraries by contributing to strategic projects, data analysis, and reporting efforts. This position assists in collecting, interpreting, and visualizing data to uncover insights, demonstrate impact, and identify areas for improvement. It also helps ensure compliance with university and national data policies, promotes best practices in data management, maintains the Libraries data dictionary, and facilitates internal and external reporting to key stakeholders. Through cross-functional collaboration, the role enhances understanding of community needs and strengthens the library’s ability to deliver effective, evidence-based services.

  • Creates and maintains a data dictionary and meta data.
  • Supports efforts to ensure that data standards are developed and maintained.
  • Ensures that the uses of data through reports and queries are accurate.
  • Supports business and system re-engineering and architecture development to define future data needs.
  • Serves as an organizational consultant on matters relating to databases by providing expertise to assist users in meeting their needs.
  • Performs other related duties as required.
  • A bachelor's degree and one year of experience in data analysis, statistics, or a related field, OR an equivalent combination of education, training, and experience.
  • Strong attention to detail and accuracy and commitment to ethical data practices
  • Strong problem-solving and critical thinking abilities
  • Ability to work collaboratively and respectfully as part of a team and effectively present data insights to diverse audiences
  • Experience in data collection, cleaning, analysis, interpretation, visualization and reporting of complex and real-world datasets
  • Working knowledge and experience in libraries or non-profit organizations
  • Experience with data visualization tools (e.g., Excel, Tableau, Power BI)
  • Knowledge of statistical analysis and modeling (e.g., SPSS)
  • Proficiency in programming languages (e.g., Python, R, SQL)
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