Senior Data Analyst - Institutional Research

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 310

Pay Range: $88,300.00 - $132,400.00

Job Description

Job Profile Summary:

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 Experience Analytics, Institutional Research, Learning Analytics, Outreach Analytics, and Student Success Analytics.

The Senior Data Analyst will reside in the Institutional Research (IR) department and 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, Data Engineering, Marketing, Internal and External Communications, the Office of the President, and others. They play an instrumental role in the measurement of the university's performance by performing ad hoc analyses, building standard reports and data visualizations, and delivering information and insights through a variety of methods and media that privilege data storytelling and compress time-to-action. They also assist in the administration of Tableau Server by implementing security and permission strategies, monitoring performance, and offering support to fellow analysts.

Essential Functions and Responsibilities:

• Data Visualization & Storytelling: Combines data analysis, visualization, and narrative structures to convey information in compelling ways that instigate deliberate action.
• Tableau Administration: Monitors load times and performance of our server. Develops and implements permission and security strategy. Enforces standardized style best practices.
• Requirement Analysis: Drives the documentation of data and analytics needs in projects of high complexity with a student and equity-centered lens, collaborating with peers, cross-functional partners, faculty staff, and leaders. Translates user stories into technical requirements.
• Expectation Management: Sets and manages expectations about analytics tasks and activities through clear, timely, and effective communication with partners and stakeholders.
• Data Querying & Transformation: 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, build forecasts, and support findings of research projects and experiments. Collaborates with Data Engineering in the development of complex ETL/ELT processes and data pipelines.
• Data Quality & Troubleshooting: 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.
• Workflow & Automation: Utilizes software, scripts, and algorithms to perform data-related tasks (e.g., importing, cleaning, transforming, analyzing, displaying) without human intervention.
• Communication: Conveys information effectively to peers, partners, and senior leaders, using a variety of resources and formats (synchronous and asynchronous, verbal and written) such as e-mails, presentations, meetings, an