Director, Supply Chain Lead Data Architect

Job Description Summary:

The Supply Chain Lead Data Architect will own the supply chain data model and the data-product architecture that enables trusted analytics, decision intelligence and data science across the North America Operating Unit. This leader will treat data as a product: defining the business purpose, consumers, ownership, quality expectations, access patterns, lifecycle and measurable value of the data assets the organization depends on. The role combines supply-chain domain mastery with enterprise data modeling, platform product strategy and modern engineering fluency. Partnering with supply chain, product, analytics, data science, Global Data and AI, Enterprise Architecture, Security, Governance and platform teams, the successful candidate will translate business decisions into interoperable, secure, governed and reusable data capabilities that improve performance and deliver measurable business value.

This is a Director-level role.

About the Role

Own Supply Chain Data Products and Enterprise Data Models

Define and execute the supply chain data-product strategy and multi-year architecture roadmap that enables analytics, reporting, decision intelligence and data science.

Develop and own the canonical supply chain data model, including conceptual, logical and physical models, shared business definitions and semantic layers for domains such as demand, supply, inventory, manufacturing, logistics, customer service and performance.

Establish reusable patterns for data products, ingestion, integration, lakehouse and warehouse platforms, APIs, events and streaming, master and reference data, semantic models, analytics and data-science consumption.

Lead architecture reviews and technology decisions, balancing decision value, interoperability, scalability, security, quality, reliability, performance, cost and speed to market.

Enable Integration Interoperability and Modern Data Engineering

Connect supply chain data requirements with enterprise platforms, applications and standards, reducing duplicate data, fragmented definitions, point-to-point integrations and technical debt.

Define integration and interoperability standards across ERP, manufacturing, warehouse, transportation, planning, customer and external-partner systems using APIs, events, common identifiers and governed data contracts.

Set engineering standards for CI/CD, Infrastructure as Code, automated testing, orchestration, observability, metadata, lineage, data quality, reliability and operational support.

• •Drive reusable frameworks, platform capabilities and product operating practices that accelerate delivery and make data easy to discover, understand, access and use responsibly.
• Improve workload performance, resilience and cost efficiency across cloud data, analytics and data-science platforms.

Translate Business Decisions into Data Requirements

• •Work with supply-chain leaders to identify the decisions that matter most, the measures that define success and the data required to improve planning, service, cost, inventory, capacity, resilience and sustainability outcomes.
• •Convert business priorities into clear data requirements, data-product backlogs, domain models, data contracts, acceptance criteria and delivery roadmaps that technical teams can execute.
• •Define patterns that enable data scientists and analysts to use governed, explainable and fit-for-purpose data, including feature-ready data, semantic models and secure self-service access.
• •Partner with data science and engineering teams to move analytical and predictive solutions from prototypes to secure, observable and reliable production capabilities.
• •Establish architecture guardrails for privacy, security, governance, model use, explainability, evaluation and appropriate human oversight where AI is applied.

Lead Cross Functional Delivery and Augmented Teams

Serve as a trusted partner to supply-chain, product, finance, operations, data science and technology leaders; communicate architecture choices, dependencies, tradeoffs, value, cost and risk in clear business language.

Lead cross-functional teams and augmented resources, setting clear outcomes, architectural guardrails, delivery expectations and quality standards while building capability in internal teams.

Facilitate alignment across organizational boundaries, resolve design decisions and sequence investments to deliver the highest-value data products first.

Improve the discoverability, accessibility, quality and usability of trusted supply-chain data so teams can move from questions to decisions more efficiently.

Drive Governance Quality Value and Enterprise Alignment

• •Establish data-product governance with clear ownership, stewardship, quality rules, service expectations, metadata, lineage, retention and least-privilege access from design through operations.
• •Partner with NAOU Digital and Data, Global Data and AI, Enterprise Architecture, Cybersecurity, Privacy, Governance and platform organizations to align supply-chain solutions with enterprise direction.
• •Establish measures and operating reviews for data quality, adoption, reuse, time to insight, delivery speed, reliability, cost, risk reduction and realized supply-chain value.
• •Build a high-performing culture focused on accountability, product thinking, value creation, inclusion, quality and continuous improvement; mentor technical talent and augmented team members.
• •Enable teams to make decisions within clear architectural guardrails and continuously improve the data-product portfolio based on stakeholder feedback and performance evidence.

What You Will Need

• •Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science or a related field, or equivalent professional experience; advanced degree is a plus.
• •7+ years of progressive experience in data architecture, enterprise data modeling, data engineering or analytics platforms, including leadership of complex enterprise-scale transformations.
• •7+ years of experience leading architects, engineers, technical leaders, augmented resources or multidisciplinary platform teams, including responsibility for talent development and delivery outcomes.
• •Demonstrated supply-chain domain expertise and experience translating supply-chain processes and decisions into data requirements, business definitions, data models and analytical products.
• •Demonstrated expertise designing enterprise conceptual, logical and physical data models; canonical data models; master and reference data; semantic models; and data products for analytics and data science.
• •Demonstrated experience designing cloud-native architectures spanning lakehouse and warehouse platforms, data products, streaming, APIs, events, semantic models and analytical consumption.
• •Experience with enterprise cloud data platforms such as Microsoft Azure, Microsoft Fabric, OneLake, Power BI and Azure Databricks, or comparable technologies.
• •Strong understanding of integration and interoperability patterns, including APIs, event-driven architecture, data contracts, common identifiers and operational-system integration.
• •Strong understanding of CI/CD, DevOps, DataOps, Infrastructure as Code, automated testing, orchestration, observability and cloud cost-management practices.
• •Deep understanding of data governance, security, privacy, metadata, lineage, data quality, stewardship, access control and data-product operating practices.
• •Demonstrated ability to influence senior business and technology leaders, lead decisions across organizational boundaries, manage augmented resources and communicate complex topics in clear business language.
• •Experience translating technology investments into measurable outcomes across data quality, reliability, adoption, reuse, speed, cost, risk and realized business value.
• •Proven track record of working independently, applying platform product strategy and fostering a growth mindset culture in