Manager Master Data Integrity

Tyson Sales and Distribution•Springdale, AR

About The Position

The Manager, Master Data Integrity, owns the accuracy, consistency, and governance of Tyson’s Sales and Customer hierarchies. This includes customer account structures (sold-to, region, chain, and parent relationships) and sales organization with account-owner alignments across SAP, Trade Management, analytics platforms, and related systems. The role is the subject matter expert and accountable owner for these data domains, working with Sales, Finance, Customer Service, Master Data Governance, Demand Planning, and IT to prevent and resolve data issues and protect the order accuracy, trade spend, and reporting that depend on them. The manager leads the end-to-end process for creating, changing, and maintaining Sales and Customer hierarchies; establishes data-quality metrics and governance standards; and assesses the downstream effects of hierarchy changes before implementation. The position represents hierarchy data requirements in system and process projects, collaborates across business and IT teams, and leads and develops a team. Success includes documented standards and repeatable change processes, measurable reductions in hierarchy-related errors and rework, 99% accuracy, minimal downstream disruption, and trusted data for business partners.

Requirements

Bachelor’s degree in Business Administration, Information Technology, or a related analytical field, or equivalent experience.
5–7 years of experience in master data management, data governance, or data quality.
Experience leading, influencing, and mentoring team members, formally or informally.
Advanced proficiency in Excel.
Strong working knowledge of SAP systems, including customer master data and hierarchy structures.
Exceptional influencing skills and ability to communicate effectively at all organizational levels.
Strong understanding of technology and data principles, with the ability to navigate complex systems.
Ability to manage data end to end, from creation and maintenance through downstream use in reporting and decision-making.
Detail-oriented, with the ability to assess broader implications of system or data changes.
Comfort challenging requests that could compromise data integrity and proposing solution-oriented alternatives.
Intellectual curiosity and commitment to continuous improvement.
Willingness and ability to perform the physical requirements of the job, with or without reasonable accommodation.

Nice To Haves

Customer or sales master data experience in SAP.
1–2 years of project or change management experience.
Familiarity with the Food Service, Retail, or Packaged Goods industries.

Responsibilities

Serve as the champion and subject matter expert for Sales and Customer hierarchy data, maintaining data interdependencies and quality across platforms.
Own the end-to-end process for creating, changing, and maintaining Sales and Customer hierarchies.
Lead commercial sales data quality management by defining and reporting metrics, identifying and resolving inconsistencies and gaps, and driving root-cause analysis to prevent recurring issues.
Assess the downstream impact of hierarchy changes—including customer reorganizations, new accounts, sales realignments, and acquisitions—on pricing, trade programs, invoicing, and reporting before implementation.
Collaborate with stakeholders across the organization to address cross-functional data requirements.
Partner with business users and IT teams to execute data changes in a controlled and well-communicated manner while maintaining data integrity.
Establish, document, and advocate for data quality standards, procedures, and governance practices, influencing stakeholders to align with best practices.
Represent hierarchy data requirements in system and process projects, including implementations, upgrades, and organizational changes.
Lead and develop a team.
Demonstrate responsible use of approved AI tools, safeguard sensitive information, follow company policies, validate AI-generated outputs, and drive accountable, effective AI adoption and use within the team.