Principal AI Data Strategist, Global Procurement

Req. ID:

JR106237 Principal AI Data Strategist, Global Procurement

Our vision is to transform how the world uses information to enrich life for all.

Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever.

Our vision is to transform how the world uses information to enrich life for all.

Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever.

The Global Procurement team is an integral engine to Micron's growth, delivering best-in-class total cost and supply chain resiliency through innovative predictive capabilities, risk analysis, and a focus on sustainability and diversity. Our business goals are directly tied to the value we place on our team members, our greatest asset. We invest in our people through a skills-based learning and development model to create clear career pathways for growth. Join us and discover why Micron's Global Procurement team is the preferred destination to ignite your talent.

As a Senior AI Data Strategist within Global Procurement, you will lead the reimagination of the procurement data landscape so that agentic AI can execute Source-to-Pay work reliably, safely, and at scale. This is a hands-on strategy and design role for a solid problem solver who thrives on untangling complex, cross-system data challenges. You will translate business intent into a data operating model where agents detect, enrich, route, and maintain data, and where humans focus on exception handling, governance, and continuous improvement.

Data readiness is the single largest constraint to unlocking agentic AI value in procurement. Success in this role is defined by whether prioritized agentic workflows actually run on trusted, agent-consumable data, and whether the data foundation continues to strengthen itself through automated feedback loops rather than manual clean-up cycles.

This is not an IT role, but you will work alongside IT, Data Excellence, Security, and Compliance groups. You will help ensure the data strategy aligns with enterprise data platforms, Enterprise Resource Planning readiness, approved connectors, and enterprise data governance standards.

# Key Responsibilities

A core expectation is to move the procurement data function from reactive clean-up to an agent-embedded, self-improving operating model.

Data Strategy and Reimagined Operating Model

• Reimagine the Data Governance and Excellence operating model with agents embedded. Redefine how policies, data quality monitoring, lineage, stewardship, and lifecycle management operate when agents handle detection, enrichment, routing, and maintenance, and where human roles shift to exception handling and governance.
• Define the target working model, the agentic patterns that apply to data governance (for example, data quality agent, lineage agent, maintenance agent, steward copilot), and a getting-started roadmap sequenced against Enterprise Resource Planning and transformation milestones.

Data Readiness for Agentic Workflows

• Partner with agent build leads to define exactly what data elements, quality thresholds, and validation rules each agent needs to operate reliably, and map every business requirement to a data source, owner, and readiness action.
• Close gaps in master data element coverage so that lineage, definitions, and ownership are complete and agent-consumable, and stand up a searchable data dictionary and glossary that both humans and agents can query.
• Design the feedback loop between agent, source system, and steward so that data validation, correction, and prevention happen without manual human touch wherever feasible.

Data Lineage, Mapping, and Technical Foundations

• Build the working-level technical foundations that make the agentic model possible: data lineage, relationships, schema, and cross-system mapping so that downstream impact is traceable before a change lands.
• Establish documentation, versioning, change history, and downstream impact analysis designed so that agents can consume and update them as first-class users of the data catalog.
• Define control points at the point of entry, and prioritize Critical Data Elements that drive downstream procurement value.

Governance, Policy, and Enterprise Partnership

• Partner with IT, Data Excellence, Security, and Compliance to align the data strategy with enterprise controls, sensitivity labels, data loss prevention requirements, and approved connectors so that agents can access the data they need without breaking guardrails.
• Contribute to lightweight, machine-readable policies and standards that agents can enforce automatically, shifting governance from static documents to executable rules.
• Establish data quality key performance indicators, service level agreements, and monitoring so quality is measurable and managed, not assumed.

Innovation, Experimentation, and Reusable Assets

• Run rapid experiments on agentic data patterns (data quality agent, lineage agent, maintenance agent, steward copilot, and similar), validate impact, and turn the best into reusable building blocks for broader reuse.
• Pilot AI-based data validation and cleansing tools and assess fit against Micron's data landscape.

Cross-Functional Leadership and Enablement

• Coach data stewards, custodians, category managers, and business owners on how to work with data in an agent-embedded operating model, including how to author machine-readable policies and how to interpret data quality signals.
• Coordinate closely with automation, agent build, and AI strategy teams, since agentic data workflows sit at the intersection of all three, and represent the data agenda in program reviews and executive readouts.

# Minimum Requirements

• Master's degree required in Information Systems, Data Science, Computer Science, Supply Chain, Business, or a similar area, or equivalent experience.
• Eight or more years of experience leading enterprise data strategy, data governance, or master data programs in a large, complex, multi-system environment (Enterprise Resource Planning, procurement, or supply chain data preferred).
• Solid problem solver with a demonstrated ability to break down ambiguous, cross-system data issues, get to root cause, design pragmatic solutions, and drive them through to implementation, even when the answer is not obvious and the environment is complex.
• Demonstrated experience designing and operationalizing data governance operating models: roles, decision rights, policies, standards, data quality key performance indicators, lifecycle, and stewardship, with measurable adoption.
• Proven track record of translating a future-state data vision into a buildable workplan with named owners, sequenced initiatives, and executive-ready deliverables.
• Hands-on experience with data lineage, data cataloging, data dictionaries and glossaries, and master data platforms (for example, Informatica, Collibra, Ataccama, or equivalent), and comfort working across Enterprise Resource Planning systems (Systems, Applications, and Products, including S/4HANA), Ariba, and analytics platforms (Snowflake, Tableau, Power BI).
• Direct experience defining data requirements for AI and agentic workflows, including data readiness assessments, feedback loops, and agent-consumable data structures.
• Strong cross-functional leadership: proven ability to influence IT, Security, Business, and external consulting partners without direct authority.

# Preferred Requirements

• Prior experience reimagining a data governance operating model with AI or agents embedded, including agentic patterns such as data quality agents, lineage agents, maintenance agents, and steward copilots.
• Experience in Source-to-Pay, Procure-to-Pay, or Supply Chain data domains (Vendor Master, Material Master, Spend, Category Taxonomy, Contracts).
• Familiarity with Enterprise Resource Planning migration data readiness, machine-readable policies, and data loss prevention and sensitivity-label impacts on AI connector access.
• Additional certifications in data management (for example, Certified Data Management Professional), AI and analytics, or procurement transformation.

Job Profile(s):

Business Intelligence Analyst 5

Relocation level: (TBD)

Before Getting Started

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