Data Platform Engineer

JOB SUMMARY

The Data Platform Engineer is responsible for designing, building, and maintaining the university’s modern data platform, including data pipelines, data models, integrations, and analytics-ready datasets. This role supports Southeastern University’s ability to make timely, accurate, and informed decisions by ensuring that institutional data is reliable, accessible, secure, and well-governed.

This position works closely with Information Technology, Enterprise Applications, Data Analytics, Institutional Research, Enrollment, Advising, Financial Aid, Student Success, Finance, Advancement, and other university stakeholders to transform complex operational data into trusted data assets. The Data Platform Engineer will play a key role in helping the university move from reactive reporting and manual data processes toward a more proactive, customer-focused, and decision-driven data culture.

The ideal candidate is technically strong, detail-oriented, service-minded, and able to bridge the gap between complex systems, institutional business processes, and practical data solutions.

ORGANIZATIONAL RELATIONSHIPS

Reports to: Executive Director Enterprise Services

Supervisory Responsibility: N/A

Indirect Supervisory Responsibility: N/A

ESSENTIAL DUTIES - May include, but is not limited to the following:

Data Platform Development

• Design, build, and maintain scalable data pipelines that move data from core university systems into the university’s data warehouse, lakehouse, or analytics environment.
• Develop and maintain data models using SQL, dbt, and related modern data platform tools.
• Create reusable, governed, and analytics-ready datasets that support reporting, dashboards, operational workflows, and institutional decision-making.
• Support the continued development of the university’s data warehouse/data lake architecture.
• Build data structures that enable self-service analytics, AI-enabled workflows, and faster access to institutional insights.

Data Integration and Transformation

• Integrate data from university systems such as the student information system, CRM platforms, LMS, finance systems, advancement systems, housing systems, and other enterprise applications.
• Transform raw operational data into clean, standardized, and trusted data models.
• Translate business rules, source system logic, and operational processes into clear data transformations.
• Partner with application owners and business stakeholders to understand data definitions, system behavior, and reporting needs.
• Identify and resolve data inconsistencies, duplicate records, missing fields, and process-driven data quality issues.

Data Governance, Quality, and Documentation

• Develop and maintain documentation for data models, pipelines, business definitions, lineage, and transformation logic.
• Implement data quality checks, validation rules, and testing practices to increase trust in university data.
• Support institutional data governance practices, including data stewardship, access control, data classification, and consistent definitions.
• Promote standards for naming conventions, version control, deployment processes, and data modeling practices.
• Ensure data solutions are secure, FERPA-aware, well-documented, and aligned with university policy.

Analytics and Business Enablement

• Partner with data analysts, report developers, and university leaders to create datasets that support accurate and timely reporting.
• Support high-priority institutional areas such as enrollment, advising, financial aid, student clearance, student success, finance, advancement, and academic operations.
• Help reduce dependency on manual spreadsheets, one-off reporting, and disconnected departmental data processes.
• Build solutions that are practical, timely, focused, and aligned with the needs of university decision-makers.
• Assist in creating data products that help departments better understand performance, operations, trends, and opportunities.
• Operational Support and Continuous Improvement
• Monitor and troubleshoot data pipelines, scheduled jobs, integrations, and platform performance.
• Respond to data issues with urgency, accuracy, and clear communication.
• Identify opportunities to automate manual processes and improve data delivery.
• Participate in platform upgrades, migrations, security reviews, and technology modernization efforts.
• Stay current with emerging trends in data engineering, analytics, AI, cloud platforms, higher education technology, and data governance.

ADDITIONAL/NON-ESSENTIAL DUTIES

All remaining duties are considered "nonessential" within the context of the ADA, which means that the function could be reassigned to another employee in order to allow a disabled individual to hold the position. Alternatively, the manager could try to provide a reasonable accommodation so that the disabled individual could perform the function.

LOCATION

Main Campus Hybrid (some remote, some main campus, and/or some other physical location)