Director, Supply Chain Lead Data Architect

The Coca-Cola Company•Atlanta, GA

6d•$171,000 - $198,000

About The Position

The Supply Chain Lead Data Architect owns the supply chain data model and data-product architecture that enable trusted analytics, decision intelligence, and data science across the North America Operating Unit. The role treats data as a product by defining its purpose, consumers, ownership, quality expectations, access patterns, lifecycle, and measurable value. It combines supply-chain domain expertise with enterprise data modeling, platform product strategy, and modern engineering fluency. The Director defines the supply-chain data-product strategy and multi-year architecture roadmap; develops canonical, conceptual, logical, and physical data models and shared business definitions; and establishes reusable patterns for data products, integration, platforms, APIs, events, streaming, master and reference data, semantic models, analytics, and data-science consumption. The role leads architecture reviews and technology decisions with attention to business value, interoperability, scalability, security, quality, reliability, performance, cost, and delivery speed. The architect connects supply-chain requirements with enterprise platforms and standards, establishes integration and engineering practices, and improves performance, resilience, and cost efficiency across cloud data, analytics, and data-science platforms. Working with supply-chain leaders and technical teams, the role translates business decisions into requirements, backlogs, models, contracts, acceptance criteria, and roadmaps. It enables governed and explainable analytical use, supports production deployment of analytical and predictive solutions, and sets guardrails for privacy, security, governance, AI use, explainability, evaluation, and human oversight. The role leads cross-functional teams and augmented resources, aligns stakeholders across organizational boundaries, communicates architecture tradeoffs and value, and sequences investments toward high-value data products. It establishes governance, ownership, stewardship, quality, metadata, lineage, retention, and access practices, and measures quality, adoption, reuse, time to insight, delivery speed, reliability, cost, risk reduction, and realized supply-chain value. The position also mentors technical talent and fosters accountability, product thinking, inclusion, quality, and continuous improvement. The position is based in Atlanta, United States, with 0–25% travel and relocation provided. Sponsorship for employment status is not offered; applicants must already be authorized to work full-time in the United States without requiring company sponsorship. The posted pay range is USD 171,000–198,000, with an annual incentive reference value of 30%.

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 ability to translate supply-chain processes and decisions into data requirements, business definitions, data models, and analytical products.
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.
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.
Must be currently authorized to work full-time in the United States and must not require company sponsorship to continue working legally in the United States.

Nice To Haves

An advanced degree.

Responsibilities

Own the supply-chain data-product strategy and multi-year architecture roadmap for analytics, reporting, decision intelligence, and data science.
Develop and maintain canonical supply-chain data models, including conceptual, logical, and physical models, shared business definitions, and semantic layers.
Establish reusable architecture patterns for data products, ingestion, integration, lakehouse and warehouse platforms, APIs, events, streaming, master and reference data, and analytical consumption.
Lead architecture reviews and technology decisions, balancing value, interoperability, scalability, security, quality, reliability, performance, cost, and speed to market.
Connect supply-chain data requirements with enterprise platforms, applications, and standards while reducing duplication, 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.
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.
Identify important supply-chain decisions, success measures, and data needs to improve 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.
Enable governed, explainable, and fit-for-purpose data use by analysts and data scientists, 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 where AI is applied.
Lead cross-functional teams and augmented resources, setting outcomes, architectural guardrails, delivery expectations, and quality standards while building internal capability.
Facilitate alignment across organizational boundaries, resolve design decisions, and sequence investments to deliver high-value data products first.
Improve the discoverability, accessibility, quality, and usability of trusted supply-chain data.
Establish data-product governance covering ownership, stewardship, quality rules, service expectations, metadata, lineage, retention, and least-privilege access.
Partner with NAOU Digital and Data, Global Data and AI, Enterprise Architecture, Cybersecurity, Privacy, Governance, and platform organizations to align 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.
Mentor technical talent and augmented team members and build a culture of accountability, product thinking, value creation, inclusion, quality, and continuous improvement.
Continuously improve the data-product portfolio based on stakeholder feedback and performance evidence.