Job Details

Pay Grade/Pay Range: Minimum: $56,600 - Midpoint: $73,600 (Salaried E9)

Department/Organization: 209202 - OTIDE Business Intelligence

Normal Work Schedule: Monday - Friday 8:00am to 5:00pm

Note to Applicants: This is a department only search. You must be a current UA employee working in the OTIDE Business Intelligence Department to be considered. Position is eligible for remote work subject to University policy.

Job Summary: The Data Analyst II gathers, audits, analyzes, and reports moderate- to high-complexity data under moderate supervision. Ensures data integrity. May build dashboards or reporting systems for end users, perform ad-hoc analyses and reporting to inform decision-making, or design predictive modeling or other data mining capabilities. May provide guidance and training to entry-level Data Analysts.

Additional Department Summary: Defines the data architecture for consistency in reporting across the department and across OTIDE for internal tracking of business operations, and externally for communications with UA partners such as College program directors. Tracks DL students, Early college students, and non-degree seeking students across multiple data sources.

Required Minimum Qualifications: Bachelor's degree and two (2) years of data analysis experience; OR master's degree and some data analysis experience.

Skills and Knowledge: Knowledge of business intelligence functions, analytics, industry standards, and best practices. Ability to understand complex relational data structures. Thorough knowledge of relevant internal databases. Ability to translate data into actionable insights. Excellent quantitative and analytical capabilities. Strong understanding of new and emerging fundraising and quantitative modeling techniques and technologies.

Preferred Qualifications: Bachelor's degree or higher in business intelligence, information sciences, statistics, or mathematics. Experience with Customer Relationship Management (CRM) software. Experience with scripting languages like Python, R and SQL and analytic tools such as Tableau or PowerBI. experience in querying, cleansing, and manipulating large data sets.