Data & ML Engineer II: Production ML, Pipelines & APIs

coke; Atlanta (GA); On-site; USD 171,000 - 198,000; Full time

Benefits offered by this job

Medical benefits

Relocation provided

Annual incentive

Job summary

The Coca-Cola Company in Atlanta is seeking a Lead Data Engineer to lead data products for a scalable, integrated digital ecosystem. You will guide engineers, build data pipelines and ML capabilities, and drive production-ready solutions in a fast-paced environment.

Responsibilities include designing REST APIs, integrating enterprise systems, and ensuring data quality and security across platforms. Strong cloud, CI/CD, and collaboration with cross-functional teams are required.

Qualifications

• 5-10 years of professional software engineering experience across application, data, or ML systems.
• Strong programming in at least one enterprise language (Python, Java, C#).
• Strong JavaScript/TypeScript and modern web development experience.
• Experience with React or a modern frontend framework.
• Experience designing REST APIs and backend services.
• Strong SQL skills and relational databases (SQL Server).
• Experience in ML frameworks (scikit-learn, XGBoost, PyTorch, TensorFlow).
• CI/CD, DevOps, and API integration across systems.

Responsibilities

• Design and develop scalable web applications using React, TypeScript/JavaScript, HTML, and CSS.
• Build backend services and REST APIs using Java/Spring Boot, Python, .NET, or equivalent.
• Develop data pipelines and ML model inference services.
• Expose model predictions via APIs to Salesforce, CRM, and web/mobile apps.
• Own lifecycle of data and model capabilities from requirements to production.

Job description

The Coca-Cola Company in Atlanta is seeking a Lead Data Engineer to lead data products for a scalable, integrated digital ecosystem. You will guide engineers, build data pipelines and ML capabilities, and drive production-ready solutions in a fast-paced environment.

Responsibilities include designing REST APIs, integrating enterprise systems, and ensuring data quality and security across platforms. Strong cloud, CI/CD, and collaboration with cross-functional teams are required.