Director, Supply Chain Lead Product Data Analyst

The Coca-Cola Company•Atlanta, GA

6d•$164,000 - $193,000

About The Position

The Lead Product Data Analyst is a Director-level role on The Coca-Cola Company’s North America Operating Unit Supply Chain Digital Enablement Team. The analyst contributes to the design, development, and adoption of enterprise-scale supply chain data products, partnering with product managers, data architects, data engineers, product designers, and business stakeholders. The role turns complex, multi-source operational data into trusted insights, scalable data products, and decision-ready reporting across sourcing, manufacturing, transportation, and customer fulfillment. Working with a high degree of autonomy in complex and ambiguous problem spaces, the analyst evaluates operational performance and opportunities, shapes data models and metrics, and informs priorities and trade-offs with evidence. The role also promotes strong analytical practices, improves measurement discipline, and enables self-service insights for product teams and stakeholders. The position actively supports data products built on Microsoft Azure Synapse Analytics and Microsoft Fabric, using PySpark notebooks, Azure Pipelines, SQL, and visualization and reporting tools such as Power BI. The role is based in Atlanta, United States, requires 0%–25% travel, and offers relocation. The company does not offer employment-status sponsorship; applicants must be authorized to work full-time in the United States without requiring sponsorship.

Requirements

Bachelor’s degree in analytics, statistics, economics, computer science, industrial engineering, supply chain, or a related quantitative field, or equivalent practical experience.
At least 7 years of experience in data analysis, analytics, or business intelligence supporting complex operational or supply chain domains.
Advanced SQL querying skills and mastery of Python or another scripting language, including PySpark.
Extensive experience with supply chain data from systems such as SAP S/4HANA, Oracle Transportation Management, Manufacturing Execution Systems, and other supply chain systems.
Knowledge of statistical methods and modeling techniques, with working knowledge of machine learning models.
Experience with data quality assessment, validation, and root cause investigation.
Ability to influence decisions through data and clear communication.
Strong collaboration skills and experience working within cross-functional product or platform teams.
Ability to work independently and foster a data-driven culture.
Applicants must be currently authorized to work full-time in the United States and must not require the company’s sponsorship to continue working legally in the United States.

Nice To Haves

Master’s degree in a related quantitative or supply chain field.
Experience supporting enterprise-scale supply chain data platforms or analytics products.
Experience applying forecasting and optimization modeling to supply chain planning, execution, or operational decision-making.
Knowledge of Azure and Microsoft Fabric storage and compute concepts that support scalable analytics products.

Responsibilities

Develop a deep understanding of end-to-end supply chain processes and data, including procurement, manufacturing, inventory, transportation, and customer fulfillment.
Analyze large, complex datasets to identify performance drivers, risks, trade-offs, and improvement opportunities across planning and execution.
Translate operational and optimization data into actionable insights that inform product strategy, roadmap decisions, and business priorities.
Investigate why performance outcomes occurred and recommend actions, while proactively surfacing high-value opportunities.
Informally mentor analysts and partners through collaboration, review, and shared best practices.
Partner with product managers and stakeholders to define product objectives, success metrics, KPIs, and outcome-based measures.
Design, maintain, and document analytical models, metrics, and business logic for scalable data products and decision intelligence.
Support the shift from static reporting to reusable, trusted data assets and analytical products.
Advise on the appropriate level of analytical rigor based on decision risk, business value, and operational impact.
Work with data architecture and engineering teams to define, validate, and evolve data models in Microsoft Azure.
Ensure analytical outputs are accurate, reliable, and aligned with supply chain process realities.
Steward data quality, metric definitions, and analytical assumptions across products.
Document data logic, metrics, and methodologies to support transparency, reuse, and auditability.
Create dashboards, analytical views, and narratives that make complex supply chain data accessible and actionable.
Communicate insights to technical and non-technical audiences, including supply chain leaders.
Raise analytical fluency across supply chain product teams and contribute to a data-informed culture.
Collaborate with analysts, data architects, data engineers, and data scientists to align insights across domains.