Data Engineer

Job Description Summary:

The Senior Manager, Software Engineer, Data Platform & Segmentation is an 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 is a hands-on technical engineering role for candidates with 3 to 6 years of experience in 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,n and quality validation. 

• Strong SQL skills and experience working with large enterprise datasets. 

• Experience designing training, evaluation,n 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, ing and production debugging. 

• Experience working with cloud-based data and ML platforms. 

• Ability to assess multiple modeling approaches and select the simplest solution that meets the business objective. 

• Strong communication skills and the ability to collaborate with product managers, business stakeholders, software engineers, and data teams 

Skills:

Pay Range:

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:

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):

City/Cities:

Travel Required:

Relocation Provided:

Job Posting End Date:

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.