Job Description

The Data Engineer supports the design, development, and operation of the university’s enterprise data platform. This position is focused on building the data pipelines, transformations and data products that power institutional analytics and decision-making on the lakehouse platform.

The Data Engineer will work closely with the Technical Product Manager for Enterprise Data, Data Governance Manager and institutional stakeholders to ingest, transform and serve data across the university’s lakehouse. The position offers meaningful hands-on work with Databricks, Delta Lake, and cloud infrastructure in a collaborative, mission-driven environment.

Duties may include:
Building and maintaining data ingestion pipelines from enterprise source systems
Transforming raw data into clean, structured data products using medallion architecture principles
Supporting the Databricks lakehouse platform including Unity Catalog administration and pipeline monitoring
Collaborating with data consumers, analysts and product managers to define and deliver data products
Documenting data pipelines, data models and platform runbooks
Other duties as assigned
Required Experience:

Two years of experience in data engineering, software development or a closely related technical role
Demonstrated experience writing Python or SQL to process, transform or analyze data
Experience building or supporting data pipelines or ETL/ELT workflows in a production environment
Required Education:

Bachelor’s degree in Computer Science, Information Technology, Management of Information Systems or a related field; four additional years of experience (for a total of six years) may substitute for a Bachelor’s Degree
Required Other:

None
Additional Preferred:

Three (3) or more years of experience in data engineering or a closely related technical field
Databricks Certified Data Engineer Associate or Professional certification
Bachelor’s degree or higher in a technical field
Experience with:
Cloud-based data lakehouse platforms (Databricks preferred)
PySpark or distributed data processing frameworks
Data modeling patterns including medallion architecture, dimensional modeling or data vault concepts
ERP data structures in student, HR or finance domains (Banner/Ellucian preferred)
Cloud platforms
Source control and collaborative development workflows (Git, pull request reviews)
Data quality frameworks and tooling (e.g., dbt tests or similar)
BI platform connectivity and supporting analytical consumers (Power BI or similar)
Higher education technology environments and constituent-sensitive data
Agile development methodologies and sprint-based delivery