Principal Data Engineer

Job Overview:Are you passionate about harnessing data to unlock business insights and drive strategic growth? At Keurig Dr Pepper, were seeking a Principal Data Engineer to lead the development and optimization of our modern data ecosystem. Youll 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 Americas leading beverage companies.As a Principal Data Engineer, you willData Engineering & Architecture: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.AI-Ready Data EcosystemsCollaborate with Data Scientists to support AI/ML pipelinesenabling 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.Leadership & Collaboration:Collaborate with cross-functional teamsincluding Solution Architects, Product Managers, Data Scientists, and Analyststo 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.Governance & Optimization: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.Who you are:You're a strategic, hands-on engineering leader who combines deep technical expertise with strong business acumen. You're passionate about solving complex data challenges, enabling AI, and mentoring teams to deliver enterprise-grade data solutions.Key Skills & Expertise:Technical Mastery (Deep, hands-on expertise expected)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.Some Experience / Familiarity With (Preferred, but not required at expert level)AI/ML platforms such as Databricks, SageMaker, AutoML, or TensorFlow.Data integration and ELT tools like Informatica Cloud, Fivetran, or Azure Data Factory.BI tools: Power BI, Tableau, or MicroStrategy.Working with SAP ERP as a data source.Cloud platforms such as Azure, AWS, or GCPespecially storage, compute, and orchestration services.DevOps practices including Git workflows, CI/CD, and automation.UNIX/Linux environments and shell scripting.Leadership & Communication:Ability to lead cross-functional teams and manage complex data initiatives from design through delivery.Excellent communicatorable 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, actionable recommendations.Problem Solving & Optimization:Demonstrated success in building and optimizing data pipelines to maximize performance and maintainability.Strong analytical and troubleshooting skills with a focus on root cause analysis and long-term solutions.Total Rewards:Salary Range: $, - $,Actual placement within the compensation range may vary depending on experience, skills, and other factorsBenefits, subject to election and eligibility: Medical, Dental, Vision, Disability, Paid Time Off (including paid parental leave, vacation, and sick time), k with company match, Tuition Reimbursement, and Mileage Reimbursement Annual bonus based on performance and eligibilityRequirements:Education:Bachelors or Masters degree in Computer Science, Data Science, Information Systems, or a related field.Experience:10+ years in data management, including integration, modeling, and optimization.Hands-on experience with Analytics tools and platforms like Snowflake or Informatica Cloud.Expert in SQL, SnowSql coding, ETL, and data warehousing.Core Skills:Expertise in conceptual architecture, data integration design, and wireframing.Familiarity with DevOps and Agile technology environments (preferred).Strong presentation and change management skills.Nice to have / Cross trained In: Exposure to SAP ERP and SAP-based data integration.Experience with DevOps, CI/CD pipelines, and Git-based workflows.Familiarity with AI/ML use cases in data operations (e.g., intelligent data quality, anomaly detection).Snowflake, dbt, or cloud platform certifications.