Senior Manager, Procurement Data Excellence & Governance

What matters most

• Transition to Agent-Embedded Governance

  Shift the procurement data function from manual, reactive clean-up to an automated, self-improving model. This requires defining machine-readable policies and data quality thresholds that AI agents can enforce, while establishing feedback loops between agents, source systems, and human stewards.

• Cross-Functional Alignment and Influence

  Partner with IT, Security, and Compliance to ensure procurement data strategy aligns with enterprise platforms and ERP readiness. You must influence these stakeholders without direct authority to implement data standards, sensitivity labels, and secure access for AI agents.

• Global Team Leadership and Development

  Lead, coach, and develop a multi-region team across U.S. and Asia. This involves building bench strength, fostering an inclusive culture, and managing talent across time zones to ensure consistent execution of the data governance framework.

• Measurable Value and KPI Ownership

  Establish and report on data quality KPIs and the business value realized from AI and automation initiatives. You are responsible for providing leadership with a single, trusted source of truth for procurement performance metrics.

Likely challenges to think through

• Balancing Innovation with Enterprise Rigor

  The role requires rapid experimentation with AI agents while maintaining strict enterprise standards for security and ERP stability. Prepare by demonstrating how you have managed pilot-to-scale transitions without compromising data integrity or compliance.

• Managing Complex Stakeholder Dynamics

  Aligning IT and Security on data access for AI agents can be difficult due to competing priorities. Prepare by highlighting your experience in translating complex data requirements into business-focused narratives that secure executive buy-in.

• Technical Integration in SAP Environments

  Integrating modern data catalog platforms like Informatica or Collibra with legacy or complex SAP S/4HANA procurement data requires deep technical expertise. Prepare by discussing your approach to mapping data lineage and cross-system dependencies in large-scale ERP environments.

Topics to prepare

• Agentic Data Governance Frameworks

  Review the shift from static, manual governance to machine-readable, agent-enforced policies. Exercise: Map out how a 'data quality agent' would interact with a Vendor Master data lifecycle, specifically defining the triggers for automated validation versus human exception handling.

• Procurement Data Domain Expertise

  Focus on Source-to-Pay data structures, specifically Vendor Master, Material Master, and Spend taxonomy. Exercise: Draft a strategy for maintaining data integrity during an SAP S/4HANA migration, focusing on how to ensure data readiness for downstream AI/ML consumption.

• Cross-Functional Influence and Change Management

  Prepare to articulate how to align IT, Security, and business stakeholders without direct authority. Exercise: Create a communication plan for an executive readout that translates technical data lineage and quality KPIs into business value narratives for non-technical stakeholders.

Skills to revise

• MDM and Data Catalog Implementation

  Demonstrate hands-on experience with platforms like Informatica, Collibra, or Ataccama. Exercise: Describe a specific instance where you configured a data catalog to enforce lineage or business glossary standards in a production environment.

• SAP S/4HANA Procurement Data Management

  Familiarity with SAP data schemas and Ariba integration. Exercise: Review your experience with cross-system mapping between SAP ECC/S4 and external analytics platforms, focusing on how you handled data sensitivity and access controls.

• Data Quality KPI Definition

  Develop measurable metrics for data health. Exercise: Define three specific KPIs for 'Material Master' data that would serve as a baseline for measuring the success of an automated, agent-based data quality initiative.

• Prepare an Agentic Governance Framework Case Study

  Develop a concise presentation detailing a past project where you transitioned from manual data stewardship to an automated or agent-embedded model. Highlight specific agentic patterns used, such as automated quality validation, lineage tracking, or steward copilots, and quantify the resulting improvements in data quality KPIs.

• Map SAP S/4HANA Procurement Data Expertise

  Create a summary of your experience managing procurement data domains (Vendor Master, Material Master, Spend, Contracts) within SAP S/4HANA or ECC environments. Be prepared to discuss how you handled data readiness for ERP migrations and how you aligned procurement data standards with enterprise-wide governance.

• Draft an Executive Narrative on AI-Ready Data Strategy

  Prepare a brief, high-level narrative explaining how you would prioritize Critical Data Elements for AI/ML readiness at Micron. Focus on how you would influence cross-functional stakeholders (IT, Security, Compliance) to adopt machine-readable policies that enable safe, automated data usage by AI agents.

• Formulate Strategic Questions for the Interviewer

  Prepare thoughtful questions regarding the current maturity of Micron's procurement data foundation, the specific AI agent use cases currently in pilot, and how the team balances cross-regional collaboration between the US and Asia to maintain consistent data standards.

• Evidence of Cross-Functional Influence

  Prepare concrete examples of how you have successfully aligned disparate teams (IT, Security, Business) around common data standards without having direct authority. Focus on your approach to change management and how you communicated the business value of data excellence to senior leadership.