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# Principal Data Engineer

- **Company:** The Coca-Cola Company
- **Location:** Atlanta, GA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Training Data, Artificial Intelligence, Business Analytics Applications, Data Analysis, Automation of Tests, Microsoft Azure, Code Generation, Code Review, Information Systems, Continuous Integration, Data as a Services, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Cursor, DevOps, Digital Technology, Distributed Computing Environment, Github, Python (Programming Language), Machine Learning, Marketing Information Systems, Metadata, Performance Tuning, Release Management, Secure Coding, SQL Databases, Azure Service Bus, Azure Data Factory, GitHub Copilot, Build Management, Adobe, Data Lakes, Pyspark, Infrastructure Automation Frameworks, Information Technology, Deployment Automation, Bicep, Cloud Optimization, Terraform, Code Restructuring, Azure Synapse Analytics, Software Version Control, Data Pipelines, Databricks
- **Published:** October 18, 2026
- **Apply:** https://dejobs.org/x/x/E94C3F663373451799D828B85C01AA04/job/

## About the Role

* Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field; advanced degree preferred.
 * 10+ years of hands-on data engineering or platform experience, including principal-level technical leadership, production delivery, and mentoring engineers.
 * Expert SQL and strong Python/PySpark skills across data modeling, ETL/ELT, distributed processing, orchestration, quality, observability, and performance tuning.
 * Deep, hands-on experience with Microsoft Azure data services, including Data Factory, Fabric, Databricks, Data Lake Storage, Synapse Analytics, Functions, or Event Hubs.
 * Strong GitHub and DevOps experience with code review, automated testing, CI/CD, infrastructure as code using Terraform or Bicep, and automated deployment.
 * Practical experience with Codex, Cursor, or GitHub Copilot, including disciplined validation of generated code, tests, security, and documentation.
 * Experience engineering consumer, media, customer, commerce, or marketing data from 1st party, 2nd party, and 3rd party sources in a matrixed organization.

## Description

The NAOU Marketing Data team is responsible for building the data foundation, engineering capabilities, and analytics solutions that enable better marketing decisions across North America. The team connects data, technology, and insights to create scalable capabilities that support consumer understanding, marketing effectiveness, measurement, personalization, and AI-driven decision-making., The Principal Data Engineer is a hands-on technical leader on the NAOU Marketing Data team who personally designs, builds, deploys, and operates scalable marketing data pipelines and curated data products on Microsoft Azure. The role converts internal and external data into trusted, governed, reusable assets for analytics, measurement, activation, personalization, and AI.

This individual contributor owns end-to-end technical delivery across pipeline engineering, data products, DevOps, reliability, and cross-functional integration. The role sets engineering standards while personally writing and reviewing code, resolving complex issues, building reusable capabilities, and mentoring engineers through hands-on guidance.

What You Will Do for Us

 * Design & Build Azure Data Pipelines: Personally design, code, test, deploy, and operate scalable batch and streaming pipelines across Azure. Integrate consumer, media, commerce, CRM, loyalty, Adobe, and enterprise data sources using reusable engineering patterns.
 * Curate Trusted, AI-Ready Data Products: Build standardized, documented datasets and data products that are accurate, discoverable, reusable, and ready for analytics, machine learning, and generative AI. Apply strong data modeling, metadata, lineage, and data contract practices.
 * Build Reusable Engineering Capabilities: Lead technical design and create frameworks, libraries, templates, and integration patterns that speed delivery. Translate requirements into production solutions; write and review Python, SQL, and PySpark code; troubleshoot complex issues; and balance scale, quality, and maintainability.
 * Establish GitHub Engineering & DevOps Practices: Use GitHub for source control, pull requests, code reviews, documentation, and collaboration. Implement automated testing, CI/CD, infrastructure as code, release management, and secure development practices.
 * Use AI-Assisted Development Responsibly: Use Codex, Cursor, and GitHub Copilot to accelerate design, coding, testing, refactoring, and documentation. Establish validation and security guardrails so AI-generated code meets enterprise engineering, privacy, and quality standards.
 * Ensure Reliability, Quality & Operational Excellence: Build monitoring, observability, alerts, data quality controls, and service expectations into every pipeline. Lead incident response and root-cause analysis while optimizing performance, scalability, security, and cloud cost.
 * Partner, Deliver & Develop Engineering Talent: Partner across Marketing, Analytics, Architecture, MarTech, and Digital Technology to deliver high-value capabilities. Mentor engineers through hands-on pairing and code reviews, raising standards and fostering accountability, curiosity, and continuous learning.