Principal Engineer, Data Operations

What You’ll Do

• Serve as the principal technical owner for Data Operations architecture, standards, and best practices across enterprise data platforms.
• Set the technical direction for data operations capabilities that support reliable, scalable, secure, and efficient data movement and processing across the organization.
• Define and evolve scalable Snowflake, dbt, semantic layer, and KPI architecture patterns that enable trusted, governed, and performant data assets.
• Lead critical design decisions for large-scale data models, metric standardization, semantic architecture, and data quality frameworks that support business and product outcomes.
• Partner with data engineering, product, analytics, and business stakeholders to translate complex requirements into scalable technical solutions and architecture recommendations.
• Drive technical alignment with the product roadmap and vision by ensuring KPI metrics, data models, and semantic definitions are well-defined, actionable, and reusable.
• Establish and advance technical standards for dbt, testing frameworks, CI/CD, Git workflows, orchestration, documentation, and operational excellence.
• Resolve complex DataOps and data architecture issues across large-scale ecosystems, including streaming, podcasting, satellite, publisher, and advertiser use cases.
• Review and guide the development of data pipeline requirements, process models, architectural recommendations, and reusable implementation patterns.
• Provide technical leadership, coaching, and mentorship to analysts, engineers, and cross-functional partners without direct performance-management responsibility.
• Create formal networks with key technical and business decision makers to influence enterprise data strategy, platform evolution, and long-term DataOps capabilities.
• Manage and analyze large-scale data volumes (1+ PB) across complex ecosystems that power streaming, podcasting, satellite, publisher, and advertiser use cases.
• Define and champion engineering standards, design principles, governance practices, and reusable patterns for data operations.
• Guide and mentor engineers and technical leaders across a wide range of audiences, raising technical judgment and operational excellence throughout the organization.
• Represent the organization’s data operations capabilities with senior stakeholders and, when appropriate, external technical communities.

What You’ll Need

• Typically requires a minimum of 10 years of related experience with a Bachelor’s degree; or 8 years and a Master’s degree; or a PhD with 6 years’ experience; or equivalent experience.
• Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Engineering, or a related field.
• Principal-level experience in data operations, data engineering, analytics engineering, systems architecture, or enterprise data platform strategy.
• Expert command of the modern data stack, including Snowflake, dbt, Git, orchestration tools, data modeling and metric governance, CI/CD practices, testing frameworks, and semantic layer platforms.
• Demonstrated ability to define technical strategy, architecture standards, and scalable implementation patterns across complex enterprise data ecosystems.
• Deep expertise in dimensional modeling, metric standardization, semantic architecture design, governed KPI frameworks, and data quality practices.
• Demonstrated success resolving critical technical issues and shaping broad architectural or design decisions.
• Proven ability to influence cross-functional analytics, product, engineering, and business stakeholders through technical strategy, architecture recommendations, and data-driven decision making.
• Experience mentoring senior analysts, engineers, or technical partners and raising technical quality across teams without direct people-management accountability.
• Excellent written and verbal communication skills, with a passion for making data accessible, actionable, and trusted at enterprise scale.
• Must have legal right to work in the U.S.