Director, Architect, Data & Analytics

Coca-Cola; Georgia; On-site; USD 180,000 - 240,000; Full time

Job summary

The Coca-Cola Company seeks a Director of NAOU Data Architecture & Engineering to lead architecture and engineering strategy for data, analytics, AI and data science capabilities across North America. You will guide complex enterprise-scale transformations and set standards for CI/CD, IaC, and MLOps.

This role partners with data scientists and AI engineers to move prototypes to secure, observable production solutions while aligning with enterprise direction and governance.

Qualifications

• Bachelor’s degree or equivalent professional experience; advanced degree is a plus.
• 12+ years of progressive experience in data architecture and analytics platforms.
• 5+ years leading architects, engineers or platform teams.
• Cloud-native architectures spanning lakehouse and warehouse platforms.
• Experience with enterprise cloud data platforms such as Azure, Fabric, OneLake, Power BI and Azure Databricks.
• Working knowledge of ML/AI lifecycles and MLOps.
• Knowledge of AI/ML technologies and frameworks such as Azure ML, Azure AI Foundry, MLflow, PyTorch, Spark ML, Semantic Kernel, LangChain.
• Knowledge of Generative AI architectures including LLMs, RAG, embeddings, vector search.
• Strong understanding of CI/CD, DevOps, DataOps, IaC, automated testing, orchestration, observability, cloud cost management.

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, IaC, automated testing, orchestration, observability, metadata, lineage, data quality and reliability.
• Drive reusable frameworks and platform capabilities that accelerate delivery across multiple business domains.
• Improve workload performance, resilience and cost efficiency across cloud data, analytics and AI platforms.
• Define architecture patterns for end-to-end ML/AI lifecycle including experimentation, training, deployment, monitoring and lifecycle management.
• Establish MLOps practices for model versioning, reproducibility, CI/CD, deployment, registries, monitoring and retraining.
• Define secure patterns for Generative AI and agentic solutions with integration points.
• Partner with data scientists to transition prototypes into production-grade solutions.
• Establish architecture guardrails for Responsible AI, privacy, security and governance.
• Improve discoverability, accessibility and quality of trusted data and AI capabilities for faster decision making.

Skills

Data architecture

Data engineering

AI architecture

MLOps practices

Cloud platforms

CI/CD

Observability

Data governance

Generative AI

Model lifecycle

Education

Bachelor's degree in CS/Engineering/IS/Data Science or related field

Advanced degree, a plus

Tools

Azure

Azure Databricks

Power BI

OneLake

Fabric

MLflow

Spark ML

LangChain

Semantic Kernel

RAG

Job description

Job Description Summary:

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 will be thought leader in architecture and engineering strategy for data, analytics, data science and AI capabilities supporting the North America Operating Unit.

Leads multiple highly complex projects and ensures the success of enterprise-level application rollouts; determines and develops architectural approaches and solutions; conducts business reviews; documents current systems and develops recommendations; may contribute in the planning of the overall organizational IT strategy. Individual contributor who guides others in resolving complex issues in specialized area based on existing solutions and procedures; works independently with guidance only in the most complex situations.

What You’ll Do for Us

• 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 Retrieval-Augmented Generation (RAG), embeddings, vector search, enterprise grounding, evaluation, model orchestration, AI agents, APIs and Model Context Protocol 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.

Requirements and Qualifications

• Bachelor's degree in computer science, Engineering, Information Systems, Data Science or a related field, or equivalent professional experience; advanced degree is a plus.
• 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-ma