Senior Analytics Engineer
Senior
Aws Athena
Aws Data Engineer
Aws Data Engineering
Business Intelligence
Data Analysis
Data Analytics
Data Architecture
Data Engineer
Data Integration
Data Pipeline
Data Platform
Data Visualization
Data Warehouse
Data Warehousing
Microsoft Fabric
Power BI
Reporting and Analytics
Snaplogic
SQL
Workday
Job Description
Join Wake Forest University as a Senior Analytics Engineer in a role built for real impact. You will partner with stakeholders across campus to design, build, and support analytics solutions, while helping modernize the data platform toward an AWS lakehouse architecture and Microsoft Fabric. This is a full-time, hybrid-by-default position where you can grow through structured learning support, contribute to high-visibility work as an individual contributor, and use your technical judgment to shape the next generation of analytics at the institution.
What you’ll do
- Work directly with stakeholders across campus to understand questions before defining the right solution, treating requirements gathering as an ongoing conversation.
- Advise units on data architecture, data management, and governance practices as they build their own analytics capability.
- Coach and support customers through implementation and adoption of analytics products and platforms.
- Design and develop end-to-end data pipelines, models, and analytic products spanning source systems including Workday (SIS, Finance, HCM).
- Write complex, performant, and readable SQL that supports maintainability by others.
- Build and tune curated data layers in AWS Lake Formation/Athena and Microsoft Fabric, delivering reporting through Power BI and other analytic tools.
- Own solutions across the lifecycle: discovery, design, build, test, deploy, support, and iterate.
- Help transition integration work from SnapLogic toward spec-driven development in Python using AI-assisted tooling.
- Contribute to decisions on the future warehouse direction, including evaluation of Redshift and Snowflake.
- Support retirement of legacy reporting (including Cognos) in a way that avoids disruption for current users.
- Participate in AI projects as they are scoped, helping differentiate improvements in real work from changes that only look beneficial.
Required qualifications
- 5+ years of hands-on experience building a data warehouse, data pipeline, or analytics solutions in a professional setting.
- Strong SQL, including the ability to write, debug, and optimize complex queries against large datasets.
- Working proficiency in Python, or evidence you can reach it 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 (including Power BI, Tableau, Cognos, Looker, or similar).
- Ability to translate between technical and non-technical audiences, with patience for working with people who are not data professionals.
- A self-starter approach: you can take an ambiguous problem, ask good questions, and make progress without a handed task list.
- Genuine interest in learning technologies you haven’t used yet.
- Bachelor’s degree.
Benefits
- Real support for learning, including training, certification, and time to explore new tools.
- Flexibility in how you work: hybrid by default, with roughly one day a week on campus.
- A small team where your work is visible and your judgment matters.
Additional information
- Full-time position.
- This role is not eligible for sponsorship of non-immigrant or immigrant visa status through Wake Forest University. All eligible applicants are encouraged to apply.
- Wake Forest University conducts background investigations and drug screens for all final staff candidates being considered for employment.
- If you need an accommodation to participate in the application or interview process, contact [email protected] or (336) 758-4700.
Technology exposure
- SQL, Python
- AWS Lake Formation, AWS Athena
- Microsoft Fabric
- Power BI
- Workday (SIS, Finance, HCM)
- SnapLogic
- Redshift, Snowflake
- Cognos, Tableau, Looker