The posting
Job Description Summary:
The Senior Manager, Software Engineer, Data Platform & Segmentation is a individual contributor accountable for the technical vision, design, and evolution of data platforms and segmentation capabilities that power Customer and Commercial product teams operating under a modern Product Operating Model.
This role functions as a hands-on technical engineer with 3 to 6 years of experience on data and machine learning engineering.
The role emphasizes deep technical expertise, product partnership, and architecture, rather than people management.
Core Accountabilities
Product Model & Discovery Partnership
- Partner closely with Product Managers, Designers, and Tech Leads to co-own outcomes, not just data assets.
- Participate actively in product discovery to ensure segmentation strategies are technically feasible, scalable, and analytically sound.
- Translate business and customer questions into durable data models and segmentation frameworks.
Data Platform & Segmentation Architecture
- Analyze and integrate structured and unstructured data from enterprise platforms, customers, and external data providers.
- Build scalable data preparation and feature engineering pipelines for ML applications.
Machine Learning
- Develop predictive and recommendation models using appropriate statistical and machine learning techniques.
- Evaluate and select appropriate approaches based on each use case, including: Classification and regression Ranking and recommendation Clustering and segmentation Time-series and forecasting Gradient-boosting and tree-based models Deep learning and transformers Computer vision Embeddings, vector search and RAG
- Build reliable training and inference pipelines for batch and near-real-time use cases.
- Develop APIs and services that expose model predictions to web, mobile, CRM, Salesforce, and other enterprise applications.
- Establish rigorous model evaluation, testing and validation practices.
Engineering Execution & Data Quality
- Build and maintain high-quality, production-grade data pipelines and services.
- Ensure strong standards for data quality, lineage, observability, and reliability.
- Implement MLOps pipelines covering training, testing, versioning, deployment and model lifecycle management.
- Monitor production models for model performance, data quality, drift and other operational issues.
- Implement appropriate retraining, rollback and model versioning strategies.
- Troubleshoot issues across data pipelines, models, inference services, APIs, and production environments.
- Create reusable ML components and patterns that can support multiple Transaction Growth use cases.
- Participate in architecture reviews, code reviews and engineering design discussions.
Microsoft Azure Data Platform & Fabric Expertise
- Design and evolve segmentation and data platform architectures leveraging Azure Data Fabric concepts, ensuring interoperability, governance, and reuse across domains.
- Apply strong architectural judgment across core Azure data products, including data ingestion, storage, processing, analytics, and activation layers.
- Optimize designs across cost, performance, latency, and scalability, using Azure-native capabilities and patterns.
- Ensure secure-by-design implementations aligned with Azure identity, access, encryption, and compliance controls.
- Partner with enterprise architecture, cloud, and security teams to ensure Azure data platform decisions align with broader enterprise strategy while preserving team autonomy.
- Stay current on Azure data platform evolution and proactively assess new capabilities for business value, not novelty.
Business Partnership & Communication
- Serve as a trusted technical partner to Customer and Commercial stakeholders.
- Communicate segmentation concepts, assumptions, and limitations in clear business language.
- Proactively surface data constraints, privacy considerations, and trade-offs to enable informed decisions.
- Support external partner and vendor conversations as a technical authority when needed.
Governance, Privacy & Compliance
- Ensure segmentation approaches comply with data privacy, consent, and regulatory requirements.
- Collaborate with Security, Privacy, and Legal teams to embed governance into platform design—not bolt it on later.
- Advocate for responsible and ethical use of customer and commercial data.
Success Measures
- Segmentation capabilities measurably improve customer engagement and commercial outcomes.
- Reduced duplication and inconsistency in segmentation logic across products.
- Improved data quality, freshness, and trustworthiness.
- Faster time-to-insight and activation for product teams.
- Platforms and models that scale with growth while controlling cost and risk.
Required Experience & Capabilities
- Bachelor’s degree in Computer Science, Engineering, Data Science, or equivalent experience.
- 3+ years of hands-on experience in data platform, analytics engineering, or backend engineering roles.
- Strong programming experience with Python or common data and ML libraries.
- Experience developing production machine learning models using frameworks such as scikit-learn, PyTorch, TensorFlow, XGBoost, or equivalent.
- Strong understanding of supervised and unsupervised learning, model selection, feature engineering and statistical modeling.
- Experience preparing large datasets for machine learning, including cleansing, transformation, feature generation and quality validation.
- Strong SQL skills and experience working with large enterprise datasets.
- Experience designing training, evaluation and inference pipelines.
- Experience deploying machine learning models into production environments.
- Practical understanding of MLOps, including experiment tracking, model versioning, CI/CD, automated testing, deployment and monitoring.
- Experience developing or integrating APIs and services used for model inference.
- Strong software engineering practices including modular design, source control, code review, automated testing and production debugging.
- Experience working with cloud-based data and ML platforms.
- Ability to assess multiple modeling approaches and select the simplest solution capable of meeting the business objective.
- Strong communication skills and the ability to collaborate with product managers, business stakeholders, software engineers and data teams.
The Coca-Cola Company will not offer sponsorship for employment status (including, but not limited to, H1-B visa status and other employment-based nonimmigrant visas) for this position. Accordingly, all 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 to work legally in the United States.
Skills:
Pay Range:
United States: 152,000 - 178,300 USD

Base pay offered may vary depending on geography, job-related knowledge, skills, and experience. A full range of medical, financial, and/or other benefits, dependent on the position, is offered.
Annual Incentive Reference Value Percentage:
15
Annual Incentive reference value is a market-based competitive value for your role. It falls in the middle of the range for your role, indicating performance at target.
Location(s):
United States of America
City/Cities:
Atlanta
Travel Required:
00% - 25%
Relocation Provided:
No
Job Posting End Date:
October 14, 2026
Our Purpose and Growth Culture:
We are taking deliberate action to nurture an inclusive culture that is grounded in our company purpose, to refresh the world and make a difference. We act with a growth mindset, take an expansive approach to what’s possible and believe in continuous learning to improve our business and ourselves. We focus on four key behaviors – curious, empowered, inclusive and agile – and value how we work as much as what we achieve. We believe that our culture is one of the reasons our company continues to thrive after 130+ years. Visit Our Purpose and Vision to learn more about these behaviors and how you can bring them to life in your next role at Coca-Cola.
We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran, and basis of disability or any other federal, state or local protected class. When we collect your personal information as part of a job application or offer of employment, we do so in accordance with industry standards and best practices and in compliance with applicable privacy laws.
