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The Coca-Cola Company

Sells soft drink concentrates to bottlers worldwide

Director - Architect - Data & Analytics

Engineering LeadershipUpdated on Nov 10
Full-timeAtlanta, GA, USA

About the Job

Requirements

  • A bachelor's degree in computer science, engineering, information systems, data science, or a related field, or equivalent professional experience; an advanced degree is a plus.
  • At least 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.
  • At least 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 artificial intelligence solution lifecycles and MLOps practices, including experimentation, deployment, model registries, monitoring, drift detection, reproducibility, and governance.
  • Knowledge of artificial intelligence and machine learning 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 artificial intelligence architectures, including large language models, retrieval-augmented generation, embeddings, vector search, enterprise grounding, AI agents, evaluation, and API- or Model Context Protocol-based integration.
  • Strong understanding of continuous integration and continuous delivery, 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.
  • 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.

Responsibilities

  • Lead architects, data engineers, platform engineers, machine learning and artificial intelligence engineers, and technical leaders responsible for building and operating enterprise data and artificial intelligence capabilities.
  • Establish engineering standards for continuous integration and continuous delivery, 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 artificial intelligence platforms.
  • Define architecture and engineering patterns for the end-to-end machine learning and artificial intelligence lifecycle, including experimentation, feature engineering, training, evaluation, deployment, inference, monitoring, and lifecycle management.
  • Establish MLOps practices for model and experiment versioning, reproducibility, continuous integration and delivery, automated deployment, model registries, monitoring, drift detection, retraining, and production support.
  • Define secure patterns for generative artificial intelligence and agentic solutions, including retrieval-augmented generation, 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 artificial intelligence 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 North America Operating Unit 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.

Desired Qualifications

  • An advanced degree.

About the Company

The Coca-Cola Company

Sells soft drink concentrates to bottlers worldwide

The Coca-Cola Company owns and markets beverage brands sold around the world, including Coca-Cola, Sprite, Fanta, Dasani, Powerade and fairlife. Its flagship cola has been sold in the United States since 1886 and anchors a portfolio spanning sparkling drinks, water, sports drinks, coffee, tea, juice and dairy. Rather than bottling most drinks itself, it sells concentrates and syrups to a franchised network of independent bottlers, which package and distribute finished beverages. It earns most revenue from those concentrate sales, plus finished drinks from bottlers it owns. The company was incorporated in 1892 and is headquartered in Atlanta, Georgia.

Company size
10,001+
Company stage
IPO
Headquarters
Atlanta, GA
Founded
1892

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