Data Engineer

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.Data pipelines and transformations for a defined domain (ingest, clean, transform, publish)Well-documented datasets and basic semantic models that enable reporting and analysisData quality checks (freshness, completeness, validity) and participation in monitoring/alertingDatasets that support machine learning use cases (e.g., feature and label tables) with clear definitionsIncremental improvements to pipeline performance, cost, and reliability with guidanceCollaboration with partners to clarify requirements and iterate on data productsWhat You Will Work OnBuild 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 WorkYou’ll be part of a dedicated, cross-functional team (Product, Design, Engineering) that is:Empowered to solve problems, not just build featuresAccountable for outcomes, not outputCollaborative by default, from discovery through deliveryContinuously learning, using data and customer insight to improveKey ResponsibilitiesPartner in Data Discovery & Solution ShapingPartner with Product, Analytics, and Engineering to understand data needs, definitions, and success metricsLearn source systems and data flows; help map entities, identifiers, and key business rulesContribute 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 usabilityBuild & Maintain Data PipelinesBuild and maintain batch and/or streaming pipelines to ingest data from source systems into our analytical platformDevelop 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 standardsSupport ML workflows by helping produce curated training datasets and feature-ready tables, following established patternsHelp monitor pipeline health and data quality; investigate failures with guidance and improve runbooks and alerts over timeOwn End-to-End Data OutcomesImplement and maintain data quality checks and basic observability (tests, audits, monitoring) for pipelines you contribute toDocument datasets and transformations (definitions, lineage, caveats) so others can confidently use and interpret the dataHelp ensure ML datasets are reproducible by supporting basic versioning/lineage and clearly documenting training data assumptionsDrive incremental improvements to reliability, performance, and cost; follow data access, privacy, and retention guidelinesContribute to a Strong Data CultureHelp evolve data standards (naming conventions, modeling patterns, documentation) to improve consistency and reusePromote a culture of data trust through quality checks, clear definitions, and thoughtful change managementCollaborate with platform partners to leverage shared tooling and improve the developer experience for data workflowsWhat We’re Looking ForStrong SQL fundamentals (joins, aggregation, window functions, performance basics)Data modeling mindset: Cares about clear definitions, grain, and making data usablePragmatic problem solving: Debugs issues, makes sensible tradeoffs, and knows when to ask for helpOwnership: Takes responsibility for assigned datasets/pipelines and follows through to productionCollaboration: Works effectively with analytics, product managers, and software engineers to deliver trusted dataMachine learning exposure (a plus): Familiarity with features/labels, experimentation, and the importance of reproducible training dataKey Qualifications2-5 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 correctnessProficiency in Python (or similar) and comfort using Git and code reviews to collaborateFamiliarity with data platforms (data warehouse/lakehouse concepts), and exposure to orchestration/ETL tools (e.g., Airflow, dbt, Spark) is a plusPreferred QualificationsExperience working with a modern data warehouse/lakehouse (e.g., Snowflake, BigQuery, Databricks) through coursework or projectsExposure to transformation and orchestration tools (e.g., dbt, Airflow) and analytics engineering practicesUnderstanding of dimensional modeling and/or event modeling concepts (fact/dimension tables, star schemas)Exposure to data quality testing, monitoring, or observability conceptsFamiliarity with data governance concepts (PII handling, access controls, retention) and a willingness to learn policiesExposure to machine learning workflows (training data preparation, feature tables, model experimentation support)Familiarity with modern engineering practices (CI/CD, testing, observability)EducationBachelor’s degree in Computer Science, Engineering, or a related fieldEquivalent practical experience is equally valuedWho Thrives HereCare about data ac