Data/Machine Learning Engineer II

The Coca-Cola Company•Atlanta, GA

1d•$171,000 - $198,000•Onsite

About The Position

Our view of digital is one of an integrated ecosystem of platforms that create value across the digital and physical world. Our digital strategy creates value not only for our consumer and customers but across our organization and system. As the Lead Data Engineer, you will be a technical lead for data products and subject matter expert to a team of data engineers. The successful candidate should be an analytical thinking, self-learner, and be able to lead a team through proactive solution development. If you have a passion for innovation, be at the forefront of our iconic digital transformation.

Requirements

5–10 years of professional software engineering experience, with experience spanning application development, data, or machine learning systems.
Strong programming experience in at least one enterprise application development language such as Python, Java, C#, or equivalent.
Strong experience with JavaScript/TypeScript and modern web development.
Hands-on experience with React or a comparable modern frontend framework.
Experience designing and developing REST APIs and backend services.
Strong SQL skills and experience with relational databases such as SQL Server.
Experience developing data pipelines, transforming datasets, and performing feature engineering.
Practical experience developing machine learning models using frameworks such as scikit-learn, XGBoost, PyTorch, TensorFlow, or equivalent.
Experience taking machine learning solutions beyond experimentation into production inference.
Experience integrating multiple enterprise systems using APIs and modern integration patterns.
Experience with automated testing, containers, monitoring, and production support.
Strong software engineering fundamentals including modular design, testing, code reviews, debugging, and maintainability.
Ability to work across Product, Data, Architecture, Business, and Engineering teams to translate requirements into production solutions.
Experience using CI/CD tools – GitHub, GoCD, Terraform and/or Azure DevOps
Solid knowledge about data modeling and architecture
Knowledge with developing applications using public cloud, preferably Azure, specially focused in Data Services (CosmosDB, DynamoDB, Databricks, Glue, DataLake, Redshift, Azure Synapse etc.)
Understand Agile Scrum, CI/CD, DevOps best practices
Solid understanding of Git-based version control

Responsibilities

Design and develop scalable web applications using React, TypeScript/JavaScript, HTML, and CSS.
Build backend services and REST APIs using technologies such as Java/Spring Boot, Python, .NET, or equivalent.
Design and develop integrations among Salesforce, web and mobile applications, CRM platforms, databases, and enterprise systems.
Work with relational databases such as SQL Server, including data modeling, queries, and application integration.
Build secure API-driven solutions using modern authentication and authorization patterns.
Work with data from enterprise data platforms, customers, and external data providers to develop intelligent product capabilities.
Develop data preparation, transformation, and feature engineering pipelines.
Build predictive, classification, ranking, recommendation, segmentation, or forecasting models based on business requirements.
Evaluate when to use traditional machine learning versus advanced techniques such as deep learning, transformers, computer vision, embeddings, vector search, or RAG.
Develop batch or online model inference pipelines and services.
Expose model predictions and recommendations through APIs for consumption by Salesforce, CRM, web, and mobile applications.
Implement model and data validation, automated testing, and quality controls.
Build CI/CD pipelines for application and machine learning workloads.
Support experiment tracking, model versioning, deployment, and lifecycle management.
Monitor applications and models for availability, performance, data quality, and model degradation or drift.
Support production solutions and troubleshoot issues across systems.
Improve scalability, reliability, security, observability, and maintainability.
Own capabilities throughout their lifecycle, from requirements through production support and continuous improvement.