Principal Engineer, Data Operations

Sirius XM•New York, NY

27d

About The Position

As the Principal Engineer, Data Operations, you will serve as a principal technical authority for enterprise Data Operations, defining scalable architecture, standards, and governance practices that enable trusted, performant, and accessible data products across SiriusXM Media. You will shape the technical strategy for Snowflake-based data models, semantic layers, KPI frameworks, and DataOps practices that support business, product, publisher, and advertiser use cases. Your work will directly influence how the organization measures success, scales data quality, and makes data-driven decisions without direct people-management responsibility.

Requirements

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.
Track record of anticipating business or technology challenges and introducing meaningful process, product, service, or platform improvements.
Demonstrated ability to guide and mentor engineers and technical leaders without relying on direct people-management authority.
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.

Responsibilities

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.