Senior Data Analyst-WGU Academy

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

As a Senior IAR Data Analyst, you'll play a pivotal role in extracting, processing, analyzing, and reporting on data to produce rigorous, actionable insights and research. This role will be a leader of the team and interact often with executive-level stakeholders, driving impactful decisions and strategies.

What You'll Do

• Drive 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.
• Set and manage expectations about analytics tasks and activities through clear, timely, and effective communication with partners and stakeholders.
• Answer complex business questions requiring extensive knowledge of the university’s data assets across several domains and departments.
• Identify adequate data sources and data sets to evaluate hypotheses, build forecasts, and support findings of research projects and experiments.
• Collaborate with Data Engineering in the development of complex ETL/ELT processes and data pipelines.
• Identify, investigate, and solve complex data issues, contributing to the accuracy, completeness, consistency, timeliness, and validity of the university’s data.
• Collaborate with Data Engineering and other data & analytics partners to define standards and best practices that increase data quality across the university.
• Combine data analysis, visualization, and narrative structures to convey information in compelling ways that instigate deliberate action.
• Utilize software, scripts, and algorithms to perform data-related tasks (e.g., importing, cleaning, transforming, analyzing, displaying) without human intervention.
• Convey 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, and workshops.
• Create and organize information about processes, projects, operations, data assets, and insights from analyses and research, making it accessible in ways that increase the university’s knowledge and efficiency.
• Write and interpret technical documentation (e.g., Entity-Relationship, Conceptual, Logical, and Physical data models).
• Contribute actively to the development of the university’s data management platforms (e.g., data dictionaries, catalogs, etc.).
• Support and accelerate other team members’ development through constructive feedback and sharing of technical and institutional knowledge.
• Drive tasks, activities, and small-scale projects with high levels of autonomy, confidence, and collaboration with peers and partners.
• Track and report own progress, dependencies, and challenges diligently. Break down complex goals into concrete tasks and activities.
• Work actively to improve own skills and knowledge through internal and external, formal and informal, structured and unstructured learning. Be a lifelong learner and embody a growth mindset. Stay abreast of innovative developments in their area of work and play an active role in deploying them at the university.
• Understand and abide by the relevant policies and methods to access, use, transform, store, and delete data in responsible, secure, and compliant ways.
• Collaborate effectively with other technical specialists (e.g., data engineers) in the construction of data