Director, Supply Chain Lead Data Architect

Coca-Cola•Atlanta, GA

About The Position

The Supply Chain Lead Data Architect owns the supply chain data model and data-product architecture that support trusted analytics, decision intelligence, and data science across the North America Operating Unit. The role treats data as a product by defining the business purpose, consumers, ownership, quality expectations, access patterns, lifecycle, and measurable value of data assets. It combines supply-chain domain expertise with enterprise data modeling, platform product strategy, and modern engineering fluency. The Director partners with supply chain, product, analytics, data science, Global Data and AI, Enterprise Architecture, Security, Governance, and platform teams to translate business decisions into interoperable, secure, governed, reusable data capabilities that improve performance and deliver measurable business value. The position leads architecture strategy and decisions, cross-functional delivery, data-product governance, and technical talent development. It also aligns supply-chain solutions with enterprise direction and establishes measures for data quality, adoption, reuse, delivery speed, reliability, cost, risk reduction, and realized supply-chain value.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related field, or equivalent professional experience.
At least 7 years of progressive experience in data architecture, enterprise data modeling, data engineering, or analytics platforms, including leadership of complex enterprise-scale transformations.
At least 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.
Expertise in enterprise conceptual, logical, and physical data models; canonical data models; master and reference data; semantic models; and data products for analytics and data science.
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 ability to work independently, apply platform product strategy, and foster a growth-mindset culture in a data-product environment.
Applicants must be currently authorized to work in the United States on a full-time basis and must not require The Coca-Cola Company's sponsorship to continue to work legally in the United States.

Nice To Haves

An advanced degree is a plus.

Responsibilities

Define and execute the supply-chain data-product strategy and multi-year architecture roadmap for 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 architecture 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 business value, interoperability, scalability, security, quality, reliability, performance, cost, and speed to market.
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 make data easier to discover, understand, access, and use responsibly.
Improve workload performance, resilience, and cost efficiency across cloud data, analytics, and data-science platforms.
Work with supply-chain leaders to identify important decisions, success measures, and data needs for planning, service, cost, inventory, capacity, resilience, and sustainability outcomes.
Translate business priorities into data requirements, data-product backlogs, domain models, data contracts, acceptance criteria, and delivery roadmaps.
Define patterns that enable data scientists and analysts to use governed, explainable, 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 into secure, observable, reliable production capabilities.
Establish architecture guardrails for privacy, security, governance, model use, explainability, evaluation, and appropriate human oversight when AI is applied.
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 by setting outcomes, architectural guardrails, delivery expectations, and quality standards, while building internal team capability.
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 reach decisions more efficiently.
Establish data-product governance with clear ownership, stewardship, quality rules, service expectations, metadata, lineage, retention, and least-privilege access throughout design and 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 architectural guardrails and improve the data-product portfolio based on stakeholder feedback and performance evidence.