Data Engineer
The Coca-Cola Company
- Atlanta, Georgia, United States
- Atlanta, Georgia, United States
À propos
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
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
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
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 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.
Equal Opportunity Employer 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.
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Compétences linguistiques
- English
Avis aux utilisateurs
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