Director, Architect, Data & Analytics

Coca-Cola•Atlanta, GA

1d•$171,000 - $198,000

About The Position

At The Coca-Cola Company, data and artificial intelligence are essential to creating connected experiences, enabling faster decisions and supporting sustainable growth. The Director, NAOU Data Architecture & Engineering is a thought leader in architecture and engineering strategy for data, analytics, data science and AI capabilities supporting the North America Operating Unit. The role leads multiple highly complex projects and enterprise-level application rollouts; determines and develops architectural approaches and solutions; conducts business reviews; documents current systems and develops recommendations; and may contribute to organizational IT strategy planning. This is an individual-contributor role that guides others in resolving complex issues in a specialized area, works independently, and receives guidance only in the most complex situations. The director leads architects, data engineers, platform engineers, ML/AI engineers and technical leaders who build and operate enterprise data and AI capabilities. The role establishes engineering standards, reusable frameworks, platform capabilities and architecture patterns across data, analytics, machine learning and AI. It partners with business, technology, governance, security and privacy stakeholders to ensure solutions are aligned with enterprise direction, secure, reliable and production-ready. Success is measured through adoption, delivery speed, reliability, quality, reuse, cost efficiency, risk reduction and realized business value. The position is based in Atlanta, Georgia, with 0%–25% travel and no relocation provided. The pay range is $171,000–$198,000 USD, with an annual incentive reference value of 30%. The company does not offer employment-status sponsorship for this position; applicants must already be authorized to work full-time in the United States without requiring company sponsorship.

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 platforms and frameworks.
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 The Coca-Cola Company's sponsorship to continue working legally in the United States.

Nice To Haves

An advanced degree is a plus.

Responsibilities

Lead architects, data engineers, platform engineers, ML/AI engineers and technical leaders responsible for 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 that accelerate delivery 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 and 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 so teams can move from questions to decisions more efficiently.
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 clear architectural guardrails.
Measure success through adoption, delivery speed, reliability, quality, reuse, cost efficiency, risk reduction and realized business value.
Lead complex enterprise-scale projects and application rollouts, develop architectural approaches and solutions, conduct business reviews, document current systems, make recommendations and potentially contribute to organizational IT strategy planning.