Data Engineer

External

Coca-cola · Us, GA

Full-timeOn-siteToday

Data ModelingdbtDocumentationMachine LearningMoveObservability

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Responsibilities

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

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Collaborative by default, from discovery through delivery

Continuously learning, using data and customer insight to improve

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

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
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 (

Benefits

Health insurance

Additional Information

Job Description Summary: 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. 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

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