Digital products play a central role in how we create value for customers, support the teams who serve them, and shape the consumer experience.

Our product organization brings together small, empowered teams that move with clarity, speed, and purpose, enabling digital to be a meaningful source of advantage across Coca-Cola’s North America Operating Unit.

Our work spans customer journeys, service delivery, sales workflows, and the platforms that connect them. We are raising our standards for product craft and rebuilding the systems behind these experiences.

• Data pipelines and transformations for a defined domain (ingest, clean, transform, publish)
• Well-documented datasets and basic semantic models that enable reporting and analysis
• Data quality checks (freshness, completeness, validity) and participation in monitoring/alerting
• Datasets that support machine learning use cases (e.g., feature and label tables) with clear definitions
• Incremental improvements to pipeline performance, cost, and reliability with guidance
• Collaboration with partners to clarify requirements and iterate on data products

What You Will Work On
Build ML-powered data products that model transaction drivers and surface optimized actions as insights to be embedded within integrated internal and external digital experiences that shape how our beverage brands activate across retail, foodservice, and digital channels. The success of our products is tied directly to measurable transaction lift at the point of sale, a primary objective of the North America Operating Unit and The Coca-Cola Company as a whole.

How We Work

You’ll be part of a dedicated, cross-functional team (Product, Design, Engineering) that is:

• Empowered to solve problems, not just build features
• Accountable for outcomes, not output
• Collaborative by default, from discovery through delivery
• Continuously learning, using data and customer insight to improve

Key Responsibilities

• Partner in Data Discovery & Solution Shaping
• Partner with Product, Analytics, and Engineering to understand data needs, definitions, and success metrics
• Learn source systems and data flows; help map entities, identifiers, and key business rules
• Contribute to data modeling and design decisions with guidance (schemas, grain, slowly changing dimensions, etc.)
• Propose simpler, more reliable approaches (e.g., reuse shared datasets, standardize definitions) to improve trust and usability

Build & Maintain Data Pipelines

• Build and maintain batch and/or streaming pipelines to ingest data from source systems into our analytical platform
• Develop transformations to clean, standardize, and enrich data using agreed-upon patterns and tools (e.g., SQL, Python, dbt)
• Contribute to pipeline orchestration and deployment (version control, code reviews, scheduled runs) and follow team standards
• Support ML workflows by helping produce curated training datasets and feature-ready tables, following established patterns
• Help monitor pipeline health and data quality; investigate failures with guidance and improve runbooks and alerts over time

Own End-to-End Data Outcomes

• Implement and maintain data quality checks and basic observability (tests, audits, monitoring) for pipelines you contribute to
• Document datasets and transformations (definitions, lineage, caveats) so others can confidently use and interpret the data
• Help ensure ML datasets are reproducible by supporting basic versioning/lineage and clearly documenting training data assumptions
• Drive incremental improvements to reliability, performance, and cost; follow data access, privacy, and retention guidelines

Contribute to a Strong Data Culture

• Help evolve data standards (naming conventions, modeling patterns, documentation) to improve consistency and reuse
• Promote a culture of data trust through quality checks, clear definitions, and thoughtful change management
• Collaborate with platform partners to leverage shared tooling and improve the developer experience for data workflows

What We’re Looking For

• Strong SQL fundamentals (joins, aggregation, window functions, performance basics)
• Data modeling mindset: Cares about clear definitions, grain, and making data usable
• Pragmatic problem solving: Debugs issues, makes sensible tradeoffs, and knows when to ask for help
• Ownership: Takes responsibility for assigned datasets/pipelines and follows through to production
• Collaboration: Works effectively with analytics, product managers, and software engineers to deliver trusted data

• Machine learning exposure (a plus): Familiarity with features/labels, experimentation, and the importance of reproducible training data

Key Qualifications

• 2-5 years of experience in data engineering, analytics engineering, or software engineering (including internships or equivalent projects)
• Ability to write production-quality SQL and create reliable transformations with attention to correctness
• Proficiency in Python (or similar) and comfort using Git and code reviews to collaborate
• Familiarity with data platforms (data warehouse/lakehouse concepts), and exposure to orchestration/ETL tools (e.g., Airflow, dbt, Spark) is a plus

Preferred Qualifications

• Experience working with a modern data warehouse/lakehouse (e.g., Snowflake, BigQuery, Databricks) through coursework or projects
• Exposure to transformation and orchestration tools (e.g., dbt, Airflow) and analytics engineering practices
• Understanding of dimensional modeling and/or event modeling concepts (fact/dimension tables, star schemas)
• Exposure to data quality testing, monitoring, or observability concepts
• Familiarity with data governance concepts (PII handling, access controls, retention) and a willingness to learn policies
• Exposure to machine learning workflows (training data preparation, feature tables, model experimentation support)
• Familiarity with modern engineering practices (CI/CD, testing, observability)

Education

• Bachelor’s degree in Computer Science, Engineering, or a related field
• Equivalent practical experience is equally valued

Who Thrives Here

• Care about data accuracy and trust, and are curious about how data is used to make decisions
• Enjoy collaborating with analytics, product, and engineering partners to clarify definitions and requirements
• Take pride in building reliable pipelines, writing tests, and leaving clear documentation for others

Who This Role Is Not For

This role may not be the right fit if you:

• Prefer to work without clarifying definitions, assumptions, or data edge cases with stakeholders
• Want to build pipelines without caring about data quality, monitoring, or downstream usability
• Avoid ownership for debugging issues, improving reliability, or documenting what you build