Lead Data Analyst - Curriculum Analytics

If you’re passionate about building a better future for individuals, communities, and our country—and you’re committed to working hard to play your part in building that future—consider WGU as the next step in your career.

Driven by a mission to expand access to higher education through online, competency-based degree programs, WGU is also committed to being a great place to work for a diverse workforce of student-focused professionals. The university has pioneered a new way to learn in the 21st century, one that has received praise from academic, industry, government, and media leaders. Whatever your role, working for WGU gives you a part to play in helping students graduate, creating a better tomorrow for themselves and their families.

The salary range for this position takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs.

At WGU, it is not typical for an individual to be hired at or near the top of the range for their position, and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is:
 

Grade: Professional 312

Pay Range: $106,700.00 - $165,400.00

Job Description

The Institutional Analytics and Research (IAR) team supports and enables most university teams, departments, and colleges to make data-informed decisions that lead to better student outcomes. Our analysts, scientists, researchers, and managers are distributed across several teams: Faculty Analytics, Institutional Research, Curriculum Analytics, Outreach Analytics, and Student Success Analytics.

Curriculum Analytics uses WGU’s rich data assets to create course-level insights and student recommendations. We design and monitor course performance metrics, support the development of effective courses and programs, and create personalized guidance. Our work reflects a strong sense of purpose and a drive to innovate to help every student succeed.

The IAR Lead Data Analyst is responsible for extracting, processing, analyzing, and reporting on data to produce rigorous, actionable insights and research. They establish and maintain strong relationships with peers and leaders across IAR, Program Development, Data Engineering, Decision Intelligence, Product Management, EdTech, and Faculty. In this role, they not only lead their own projects but also act as a key collaborator and informal leader within the Curriculum Analytics team—supporting Analysts and Data Scientists, maintaining visibility into team-wide efforts, and helping to align priorities across initiatives. They perform ad hoc analyses, build standard reports and data visualizations, and deliver information through methods and media that emphasize data storytelling and compress time-to-action. With strong presentation skills and a clear, compelling communication style, the Lead Analyst is often called upon to represent the team, share insights with stakeholders, and advocate for data-informed decision-making across the organization. 

Essential Functions and Responsibilities

• Drives the documentation of data, analytics, and research needs in projects of high complexity with a student and equity-centered lens, collaborating with peers, cross-functional partners, faculty staff, and leaders.  Leads the translation of user stories into technical requirements.
• Sets and manages expectations about complex analytics tasks and activities through clear, timely, and effective communication with partners and stakeholders.
• Answers complex business questions requiring extensive knowledge of the university’s data assets across several domains and departments.
• Identifies adequate data sources and data sets to evaluate hypotheses and produce forecasts.
• Collaborates with Data Engineering in the development of complex ETL/ELT processes and data pipelines.
• Identifies, investigates, and solves complex data issues, contributing to the accuracy, completeness, consistency, timeliness, and validity of the university’s data.
• Collaborates with Data Engineering and other data & analytics partners to define standards and best practices that increase data quality across the university.
• Combines data analysis, visualization, and narrative structures to convey information in compelling ways that instigate deliberate action.
• Participates in the