Data Engineer

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.

Role

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. In this role, you will build, own and help transform:

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

minimum of 2+ 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

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.

Agile Methodology, Business Requirements, Communication, Computer Programming, Configuring (Inactive), Data Analysis, Financial Processing, Information Systems, Software Development, Structured Query Language (SQL), Systems Analysis, Systems Development Lifecycle (SDLC), Teamwork, Test Environments, Troubleshooting, Waterfall Model, Workflow Management

Pay Range

United States of America: 124,600 USD - 148,200 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:

Yes

Job Posting End Date

June 24, 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.

Pay Range:United States of America: 0 USD - 0 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.

Long-term Incentive Reference Value Percentage:0 - 20

Long-term Incentive reference value is a market-based competitive value for your role