Director of Advancement Analytics and Insights

California State University•San Jose, CA

15d•$5,053 - $16,221

About The Position

The Director of Advancement Analytics and Insights is a high-level strategist responsible for designing, deploying, and leading comprehensive business intelligence and analytics functions within University Advancement. This role serves as the primary architect of data-driven fundraising frameworks at San José State University, turning complex data into actionable insights, delivering dashboards, forecasting, and strategic analysis that guide leadership and teams across the division. The Director oversees a comprehensive philanthropy analytics strategy to support fundraising performance, engagement metrics, and campaign success, and data-informed decision-making across the division. The incumbent acts as a critical bridge between data structure (Advancement IT), data collection (Gifts and Records Administration), and data application (Prospect Research and Management, Development, Alumni Engagement), successfully championing a culture of data literacy, evidence-informed engagement, and proactive analytics across University Advancement.

Requirements

Ability to communicate with constituents in a professional and respectful manner
Working knowledge of, or the ability to develop proficiency in, the appropriate and ethical use of emerging technologies, including artificial intelligence (AI), in higher education and administrative contexts
Ability to apply emerging technologies to support operational effectiveness, data-informed decision-making, and service delivery to students, faculty, and staff
Demonstrated ability to support continuous improvement and organizational learning through the responsible application of technology within assigned areas of responsibility
Deep knowledge of predictive modeling, data mining, multivariate regression, and fundraising forecasting techniques within a higher education environment
Mastery of visualization software (specifically Microsoft Power BI) and the ability to construct secure, scalable row-level security dashboard architectures
Comprehensive knowledge of relational database structures and workflows inside complex advancement CRMs (such as Salesforce, Blackbaud CRM/NXT, Affinaquest, or Ellucian)
Strong understanding of CASE Reporting Standards and AASP data metric models
Superior capability to synthesize complex data matrices into narrative presentations and clear policy recommendations for university leadership
Ability to apply emerging technologies to support operational effectiveness, data-informed decision-making, and service delivery to students, faculty, and staff
Demonstrated ability to support continuous improvement and organizational learning through the responsible application of technology within assigned areas of responsibility
Ability to evaluate potential applications, promote responsible, ethical, and equitable use, and ensure alignment with institutional policies, regulatory requirements, and strategic priorities
Demonstrated ability to manage multi-phase analytics projects independently under tight deadlines while successfully driving tool adoption among resistant users
Exceptional judgment regarding donor data privacy laws (FERPA, GDPR, CCPA) and an uncompromised commitment to professional ethics
Bachelor’s degree from an accredited institution in Data Science, Business Intelligence, Statistics, Computer Science, Economics, or a related field
Five (5) years of progressively responsible professional experience in data analysis, business intelligence, or institutional analytics
Experience leading large-scale analytics initiatives or acting as a primary system analyst/strategist in a complex institutional or corporate environment

Nice To Haves

Master’s degree or higher in Data Science, Analytics, Business Administration (MBA), or an equivalent field
Seven (7) or more years of experience in higher education advancement services, prospect development analytics, or public university foundation data systems
Experience with fundraising principles and methods
Direct experience supporting multi-million fundraising initiatives using predictive fundraising pipelines
Experience with the California State University system, auxiliary organization models, and campus data compliance standards
Experience with SQL for data extraction, Python or R for statistical data modeling, and experience utilizing generative AI plugins for prompt engineering and rapid analytics

Responsibilities

Lead the multi-year roadmap for advancement analytics, aligning data initiatives with philanthropic and engagement pipelines, university campaigns, and divisional priorities and performance goals
Serve as the division’s lead business intelligence analyst, leveraging data-driven insights to inform decision-making, identify opportunities, and drive strategic initiatives across departments
Partners with teams on division-wide data operations, guiding divisional staff, partners, and vendors to ensure consistent, high-quality analytical standards in accordance with CASE, AASP, and industry best practices
Design, implement, and standardize key performance indicators (KPIs), performance dashboards, and accountability frameworks for University Advancement teams and leadership
Design, build, and continuously optimize a comprehensive institutional suite of interactive dashboards, reports, and data outputs that track philanthropic productivity, pipeline velocity, and fundraiser production
Oversee the delivery of automated and self-service analytics reports to front-line gift officers and academic leaders, ensuring visual and data consistency
Translate highly complex multivariate data sets and data science configurations into clear, narrative-driven business insights tailored for non-technical internal users and foundation boards
Develop, deploy, and validate advanced predictive algorithms and statistical models (e.g., donor propensity, major gift affinity, and lifetime value modeling) to uncover untapped donor capacity
Build data-driven revenue projection and multi-year forecasting models to determine target objectives and track pipeline gaps
Integrate external wealth screening and relationship mapping data directly into automated internal scoring models