Data Quality & Governance Analyst; Nashville, TN

The Global Program Controls organization provides governance, reporting, controls processes, cost management support, and portfolio visibility to help Oracle Cloud Infrastructure deliver global data center programs with greater consistency, transparency, and execution discipline.

We areseekinga detail-oriented, analytical Data Quality & Governance Analyst to strengthen enterprise data integrity and help turn data into a trusted strategic asset. This individual contributor will lead hands-on data-quality initiatives across master data environments,identify and resolve complex data inconsistencies, andestablishsustainable practices that improve efficiency, reduce risk, and enable smarter, data-driven decisions.

The ideal candidate thrives on solving difficult data challenges and understands that data is more than a technical requirement—it is foundational to operational excellence, financial accuracy, compliance,stakeholder experience, and business growth. This role is well suited to someone with a strong background in data-intensive, regulated, or financial-services environments.

• Lead data-quality assessments and improvement initiatives across enterprise and master data domains.
• Profile,validate, cleanse, reconcile, and monitor data toidentifygaps, duplicates, inconsistencies, and integrity issues.
• Investigate root causes of data-quality problems and coordinate practical, sustainable resolution with business and technical stakeholders.
• Serve as a data steward for assigned data domains, helping define ownership, standards, controls, business rules, and quality expectations.
• Develop andmaintaindata-quality metrics, scorecards, dashboards, exception reports, and remediation tracking.
• Partner with business teams, finance,delivery, technology, and risk/compliance stakeholders to improve data governance practices.
• Support the design and maintenance of unified data models, common definitions, andmaster-data management processes.
• Document data flows, critical data elements, validation rules, business definitions, lineage, and operating procedures.
• Improve data-entry processes and controls to prevent errors at the source and promote consistent adoption across teams.
• Use data analysis toidentifyprocess-improvement opportunities that reduce manual effort, improve accuracy, and strengthen internal controls.
• Support audit, regulatory, reporting, and operational requirements by ensuring data is complete,accurate,timely, and traceable.
• Translate complex data issues into clear, actionable recommendations for both technical and non-technical audiences.