Director, Architect, Data & Analytics

The Coca-Cola Company•Atlanta, GA

2d•$171,000 - $198,000

About The Position

The Director, NAOU Data Architecture & Engineering will provide thought leadership in architecture and engineering strategy for data, analytics, data science, and AI capabilities supporting The Coca-Cola Company’s North America Operating Unit. The role leads complex projects and enterprise-level application rollouts, develops architectural approaches and solutions, conducts business reviews, documents current systems, and makes recommendations. It may also contribute to overall organizational IT strategy. This is an individual-contributor role that guides others in resolving complex issues and works independently, seeking guidance in the most complex situations. The position leads architects, data engineers, platform engineers, ML/AI engineers, and technical leaders who build and operate enterprise data and AI capabilities. It establishes engineering standards, reusable frameworks, platform capabilities, cloud architecture patterns, and practices across the ML and AI lifecycle, including MLOps and Generative AI. The role partners with business, technology, governance, security, privacy, and enterprise teams to deliver secure, reliable, responsible, and production-grade solutions. It also mentors technical talent and measures outcomes such as adoption, delivery speed, reliability, quality, reuse, cost efficiency, risk reduction, and realized business value. The role is based in Atlanta, United States, with 00%–25% travel required and no relocation provided. Applicants must already be authorized to work full time in the United States and must not require Coca-Cola sponsorship to continue working legally in the United States. The position offers a base pay range of $171,000–$198,000 and 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.
12+ years of progressive experience in data architecture, data engineering, analytics platforms, data science platforms, AI architecture, or enterprise technology, including leadership of complex enterprise-scale transformations.
5+ years of experience leading architects, engineers, technical leaders, or multidisciplinary platform teams, including responsibility for talent development and delivery outcomes.
Demonstrated experience designing cloud-native architectures spanning lakehouse and warehouse platforms, data products, streaming, APIs, 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.
Working knowledge of machine learning and AI solution lifecycles and MLOps practices, including experimentation, deployment, model registries, monitoring, drift detection, reproducibility, and governance.
Knowledge of AI/ML technologies and frameworks such as Azure Machine Learning, Azure AI Foundry, MLflow, PyTorch, Spark ML, Microsoft Semantic Kernel, LangChain, or comparable technologies.
Knowledge of Generative AI architectures, including LLMs, RAG, embeddings, vector search, enterprise grounding, AI agents, evaluation, and API/MCP-based 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, quality, access control, model governance, and Responsible AI principles.
Demonstrated ability to influence senior business and technology leaders, lead decisions across organizational boundaries, and communicate complex topics in clear business language.
Experience translating technology investments into measurable outcomes across reliability, adoption, speed, cost, risk, and business value.
Applicants must be currently authorized to work in the United States on a full-time basis and must not require Coca-Cola sponsorship to continue working legally in the United States.
Skills listed: Agile Methodology, Analytics, Artificial Intelligence (AI), Business Processes, Cloud Platform, Communication, Data Analytics, Data Flow Diagram, Data Science, Data Strategies, DevOps, Digital Technology, Digital Transformation, Emerging Technologies, Enterprise Architecture Framework, Enterprise Content Management (ECM), Generative AI, Kotlin Programming Language, Microsoft Office, Microsoft SharePoint, Object-Oriented Programming (OOP), Technical Solutions, Technology Strategies, and User Experience (UX).

Nice To Haves

An advanced degree is a plus.

Responsibilities

Lead architects, data engineers, platform engineers, ML/AI engineers, and technical leaders building and operating enterprise data and AI capabilities.
Establish engineering standards for CI/CD, Infrastructure-as-Code, automated testing, orchestration, observability, metadata, lineage, data quality, reliability, and operational support.
Drive reusable frameworks, golden paths, and platform capabilities across commercial, customer, consumer, finance, supply chain, operations, and digital initiatives.
Improve workload performance, resilience, and cost efficiency across cloud data, analytics, and AI platforms.
Define architecture and engineering patterns for the end-to-end ML and AI lifecycle, including experimentation, feature engineering, training, evaluation, deployment, inference, monitoring, and lifecycle management.
Establish MLOps practices for model and experiment versioning, reproducibility, CI/CD, automated deployment, model registries, monitoring, drift detection, retraining, and production support.
Define secure patterns for Generative AI and agentic solutions, including RAG, embeddings, vector search, enterprise grounding, evaluation, model orchestration, AI agents, APIs, and MCP integration where appropriate.
Partner with data scientists and AI engineers to transition prototypes into secure, observable, reliable, production-grade solutions.
Establish architecture guardrails for Responsible AI, privacy, security, model governance, explainability, evaluation, and appropriate human oversight.
Improve the discoverability, accessibility, quality, and usability of trusted data and AI capabilities.
Communicate architecture choices, dependencies, value, cost, and risk in clear business terms to senior leaders and cross-functional stakeholders.
Partner with Global Data & AI, Enterprise Architecture, Cybersecurity, Privacy, Governance, and platform organizations to align NAOU solutions with enterprise direction.
Mentor and develop technical talent, strengthen succession pipelines, and enable teams to make decisions within architectural guardrails.
Measure success through adoption, delivery speed, reliability, quality, reuse, cost efficiency, risk reduction, and realized business value.
Lead complex projects and enterprise-level application rollouts; develop architectural approaches and solutions, conduct business reviews, document current systems, and make recommendations.
Contribute to planning the overall organizational IT strategy as needed.