Data Engineer

Coca-Cola•Atlanta, GA

22h•Onsite

About The Position

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. This role involves building 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.

Requirements

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

Nice To Haves

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

Responsibilities

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.
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.