Advancement Data Operations & Technology Lead

USF Advancement connects the university with donors who want to make a difference by providing philanthropic support to promote student success, research, academic excellence and other strategic priorities. With an endowment that now exceeds $700 million in value, our dedicated team of approximately 200 professionals, including 60 fundraisers, encompasses alumni, development and foundation operations. The team is responsible for raising more than $150 million annually and has broken records year after year with more than $184 million raised in fiscal year 2025.

The Senior Data Operations & Integrity Lead is the primary operational owner responsible for the execution, stability, and ongoing integrity of Advancement operational data.

This role institutionalizes critical data operations knowledge, transforming manual and person-dependent practices into sustainable, automated, and scalable capabilities. This position is accountable for data integrity both within the CRM and as information moves across platforms, integrations, and vendors.

Working hands-on within Advancement systems and data environments, the Lead ensures constituent, gift, designation, and engagement data are accurate, reliable, auditable, and trusted across the division. The role emphasizes data pipelines, data quality controls, and data monitoring so that data integrity improves continuously without becoming a manual bottleneck.

While this position does not define enterprise data policy, it exercises significant judgment in applying standards, resolving data issues, and determining when escalation is required to protect institutional data trust and operational continuity.

Data Operations & Execution Across the Advancement Ecosystem

• Serve as the primary operational owner for Advancement data as constituent, gift, and engagement records are created, updated, moved, and consumed across core systems, including but not limited to the CRM.
• Execute, monitor, and support day‑to‑day data operations spanning CRM records, integrations, imports, and downstream platforms that rely on Advancement data.
• Support scheduled data cleanup and remediation efforts, correcting and documenting identified data quality issues on an ongoing cadence.
• Investigate and resolve data issues end‑to‑end while identifying opportunities to eliminate recurrence through automation or improved process design.
• Apply established data standards and operational guardrails consistently in daily execution across systems. 

Data Imports, Integrations, and Modern Data Pipelines

• Execute and improve data pipelines and integrations that export Advancement data and move it reliably between systems and to vendors.
• Execute and oversee routine and high‑volume data imports, updates, and corrections.
• Validate data accuracy, attribution, and compliance through automated controls, reconciliation checks, and exception handling.
• Identify recurring integration issues and recommend systemic improvements rather than one‑off manual fixes.

Data Integrity, Quality, and Automated Monitoring

• Establish and operate data quality and integrity controls at the dataset, pipeline, and application level.
• Run routine data audits and validation checks including rules, thresholds, reconciliations, exception reporting, and alerts across the data ecosystem.
• Leverage automation and AI‑assisted tools to detect data inconsistencies, duplicate, and missing values.
• Proactively address root causes of data issues instead of reacting to downstream reporting, audit, or analytics failures.
• Support gift processing accuracy, financial reconciliation, and audit readiness through disciplined, repeatable data operations.

Business Partnership & Issue Intake

• Act as the primary point of contact for Advancement operational data‑related requests.
• Translate business needs into executable, scalable data operations solutions that improve reliability and predictability.
• Set clear expectations for scope, timing, dependencies, and escalation paths.
• Reduce ad hoc escalation by creating reliable, predictable execution models for data operations.
• Produce standard and ad hoc reports, lists, and data extracts.

Documentation & Knowledge Management

• Maintain and evolve procedural documentation for data operations, workflows, and data quality controls.