Data Engineer

Overview 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 become 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 transform data pipelines and transformations for a defined domain (ingest, clean, transform, publish). You will also create well‑documented datasets and basic semantic models that enable reporting and analysis, and implement data quality checks (freshness, completeness, validity) and monitoring/alerting. Additionally, you will develop datasets that support machine learning use cases (e.g., feature and label tables), make incremental improvements to pipeline performance, cost, and reliability, and collaborate with partners to clarify requirements and iterate on data products. Key Responsibilities Partner in Data Discovery & Solution Shaping: Collaborate with Product, Analytics, and Engineering to understand data needs, definitions, and success metrics; learn source systems and data flows; map entities, identifiers, and key business rules; contribute to data modeling and design decisions; propose simpler, more reliable approaches to improve trust and usability. Build & Maintain Data Pipelines: Build and maintain batch and/or streaming pipelines to ingest data from source systems into the analytical platform; develop transformations that clean, standardize, and enrich data using agreed‑upon patterns and tools (SQL, Python, dbt); contribute to pipeline orchestration and deployment (version control, code reviews, scheduled runs). Support ML Workflows: Produce curated training datasets and feature‑ready tables; 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); document datasets and transformations (definitions, lineage, caveats); ensure ML datasets are reproducible through versioning/lineage and clear documentation of training data assumptions. Drive Incremental Improvements: Enhance reliability, performance, and cost of data pipelines while following data access, privacy, and retention guidelines. Contribute to a Strong Data Culture: 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. Qualifications Minimum of 2+ years of experience in data engineering, analytics engineering, or software engineering (including internships or equivalent projects). Strong SQL fundamentals (joins, aggregation, window functions, performance basics). Proficient in Python (or similar); comfortable using Git and participating in code reviews. Experience with data warehouses/lakehouses (e.g., Snowflake, BigQuery, Databricks) and orchestration/ETL tools (Airflow, dbt, Spark) is a plus. Understanding of dimensional or event modeling concepts (fact/dimension tables, star schemas). Experience with 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) is advantageous. Familiarity with modern engineering practices (CI/CD, testing, observability). Bachelor’s degree in Computer Science, Engineering, or a related field (equivalent practical experience is equally valued). What This Role Is Not For Preferring to work without clarifying definitions, assumptions, or data edge cases with stakeholders. Building pipelines without caring about data quality, monitoring, or downstream usability. Avoiding ownership for debugging issues, improving reliability, or documenting work. Location United States of America – Atlanta. 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. #J-18808-Ljbffr