Principal Data Engineer

Job Overview

Are you passionate about harnessing data to unlock business insights and drive strategic growth? At Keurig Dr Pepper, we’re seeking a Principal Data Engineer to lead the development and optimization of our modern data ecosystem. You’ll play a pivotal role in designing scalable data solutions, enabling advanced analytics, and mentoring teams across disciplines to maximize the value of data. Join our innovative team and help shape the future of enterprise data at one of North America’s leading beverage companies.

• Architect and implement scalable data pipelines and transformation workflows using dbt, SnowSQL, and cloud‑native technologies.
• Design, build, and maintain enterprise‑grade data lakes, data warehouses, and transformation layers to support analytics and machine learning.
• Implement best practices for data ingestion, modeling, quality assurance, and lineage to ensure trusted and consistent data delivery.
• Create reusable frameworks and tools for automated deployment, monitoring, and lifecycle management of data assets.
• Optimize data platforms to deliver reliable, performant, and cost‑effective analytics infrastructure.

• Collaborate with Data Scientists to support AI/ML pipelines—enabling efficient feature engineering, model training, and real‑time inferencing.
• Integrate AI‑driven capabilities such as anomaly detection, intelligent alerting, and natural language enrichment into data workflows.

• Collaborate with cross‑functional teams—including Solution Architects, Product Managers, Data Scientists, and Analysts—to align on data strategy and business outcomes.
• Technically lead product teams involving external partners, ensuring timely delivery of high‑quality data solutions.
• Provide technical thought leadership, guiding project teams and business units through architectural decisions and best practices.
• Facilitate cross‑functional alignment to ensure scalable and sustainable data engineering practices.

• Define and enforce engineering standards and best practices across the analytics ecosystem.
• Lead architectural design reviews, ensuring technical rigor and adherence to change control processes.
• Continuously assess and optimize the performance, reliability, and scalability of the data platform.
• Contribute to roadmap development and long‑term strategy for the enterprise analytics platform.

• Expert‑level knowledge of Snowflake architecture, SnowSQL, and data transformation workflows.
• Advanced proficiency with dbt for modeling, testing, versioning, and orchestrating ELT pipelines.
• Strong command of SQL, Python, and scalable data pipeline development.
• Proven experience designing and managing enterprise data warehouses and cloud‑native data platforms.
• Deep understanding of modern data modeling techniques (e.g., dimensional, data vault, star/snowflake schemas).
• Experience delivering platforms that support advanced analytics and machine learning solutions.
• Solid grasp of data architecture frameworks such as Data Warehouses, Data Lakes, and Data Hubs.
• Experience with commercial data science tools like KNIME, Alteryx, or similar platforms.

• AI/ML platforms such as Databricks, Sage Maker, AutoML, or Tensor Flow.
• Data integration and ELT tools like Informatica Cloud, Fivetran, or Azure Data Factory.
• BI tools:
  Power BI, Tableau, or Micro Strategy.
• Working with SAP ERP as a data source.
• Cloud platforms such as Azure, AWS, or GCP—especially storage, compute, and orchestration services.
• Dev Ops practices including Git workflows, CI/CD, and automation.
• UNIX/Linux environments and shell scripting.

• Ability to lead cross‑functional teams and manage complex data initiatives from design through delivery.
• Excellent communicator—able to convey complex technical concepts to both technical and non‑technical audiences.
• Proven track record of influencing architectural direction, mentoring team members, and driving business‑aligned outcomes.
• Skilled at deriving insights from data and translating them into clear,…