Senior Analytics Engineer

• Work directly with stakeholders across campus to understand their questions before defining a solution - requirements gathering is a conversation, not a ticket.
• Advise on data architecture, data management, and governance practices for units building their own capability.
• Coach and support customers through implementation and adoption. Service is a core part of this job, not an afterthought.

Build the solutions

• Design and develop data pipelines, models, and analytic products spanning our source systems - Workday (SIS, Finance, HCM) among them.
• Write the SQL that does the heavy lifting: complex, performant, and readable enough that the next person can maintain it.

Build and tune curated data layers in AWS Lake Formation/Athena and Microsoft Fabric, and deliver reporting through Power BI and other analytic tools. Own solutions through the full lifecycle - discovery, design, build, test, deploy, support, iterate.

Modernize the platform

• Help move our integration work from SnapLogic toward spec-driven development in Python using AI-assisted tooling.
• Contribute to decisions on our future warehouse direction, including evaluation of Redshift and Snowflake.
• Help retire legacy reporting (Cognos) responsibly, without disrupting the people who depend on it today.
• Take on AI projects as we scope them, and help us separate what genuinely improves our work from what only looks like it does., This position profile identifies the key responsibilities and expectations for performance. It cannot encompass all specific job tasks that an employee may be required to perform. Employees are required to follow any other job-related instructions and perform job-related duties as may be reasonably assigned by his/her supervisor.

In order to provide a safe and productive learning and living community, Wake Forest University conducts background investigations and drug screens for all final staff candidates being considered for employment.

A cover letter is required for all positions; optional for facilities, campus services, and hospitality roles unless otherwise specified., * Five or more years of hands-on experience building a data warehouse, data pipeline, or analytics solutions in a professional setting.

• Strong SQL. This is the non-negotiable one - the ability to write, debug, and optimize complex queries against large datasets.
• Working proficiency in Python, or clear evidence you can get there quickly.
• Experience with a cloud data platform (AWS, Azure, or GCP) and modern warehouse or lakehouse patterns.
• Experience building reports and dashboards in a modern BI tool (Power BI, Tableau, Cognos, Looker, or similar).
• The ability to translate between technical and non-technical audiences - and the patience to do it well with people who are not data professionals.

A self-starter’s instinct: you can pick up an ambiguous problem, ask good questions, and make progress without being handed a task list. Genuine interest in learning technologies you haven’t used yet.

• A bachelor’s degree. We’re open on the field of study - we’ve seen strong analytics people come from music, philosophy, and biology as often as from computer science.

Preferred Education, Knowledge, Skills, Abilities:

• Experience with Workday, particularly Prism or Reports

Higher education experience, or experience with student, finance, or HR data domains Microsoft Fabric, AWS Lake Formation, or Athena

Integration platform experience (SnapLogic, Informatica, Fivetran, dbt, Airflow) Practical experience applying LLMs or AI agents to engineering or analytics work

• Data governance, data quality, or master data management experience

Accountabilities:

Individual contributor who works as part of a team, applying established standards and developing their skills. Receives guidance and direction from senior team members and management on more complex tasks

We are a small team of analytic practitioners inside Information Systems, and we serve the entire university - academic units, enrollment, advancement, finance, HR, and student life. Some weeks that means sitting with a stakeholder to shape how their data should be structured. Other weeks it means building the pipeline, the model, and the dashboard end to end.

Nobody on this team hands work over a wall. You will meet the stakeholder, understand the problem, design the solution, build it, and stay with it through adoption. If that scope sounds like a lot, it is - and it’s the reason the work is interesting.

We are also in the middle of a genuine platform shift. We’re moving from a legacy reporting estate toward a modern lakehouse architecture on AWS and Microsoft Fabric, and replacing low-code integration with spec-driven development in Python.

Alongside that, we’re early in our AI journey - deliberately so. We have projects scoped and moving forward now, and we’re working out what AI actually changes about this craft, from AI-assisted engineering through to agentic analytics for our stakeholders. We’re not promising you a mature AI practice to plug into. We’re offering something better: a chance to help build one.

What we offer

• A small team where your work is visible and your judgment matters
• Room to shape a platform migration rather than maintain a finished one
• Real support for learning - training, certification, and time to explore new tools
• Flexibility in how you work: hybrid by default, with roughly one day a week on campus
• The breadth of a university: no two stakeholder problems look alike