Data Engineer

Teamwork makes the stream work.

Roku is changing how the world watches TV

Roku is the #1 TV streaming platform in the U.S., Canada, and Mexico, and we've set our sights on powering every television in the world. Roku pioneered streaming to the TV. Our mission is to be the TV streaming platform that connects the entire TV ecosystem. We connect consumers to the content they love, enable content publishers to build and monetize large audiences, and provide advertisers unique capabilities to engage consumers.

From your first day at Roku, you'll make a valuable - and valued - contribution. We're a fast-growing public company where no one is a bystander. We offer you the opportunity to delight millions of TV streamers around the world while gaining meaningful experience across a variety of disciplines.

What you'll be doing

• Lead technical architecture, design, and development of enterprise data quality, discovery, and governance frameworks.
• Establish and enforce enterprise-wide data governance standards, policies, tooling, and processes to ensure accurate, compliant, and discoverable data assets across Roku.
• Lead design and implementation of custom integrations within data discovery frameworks like Datahub for metadata-driven lineage and search.
• Develop AI/ML-based solutions for data quality, anomaly detection, and automated data discovery.
• Extend and customize open-source data quality frameworks and tools like Deequ and Great Expectations to ensure quality across critical datasets.
• Design and implement robust batch and real-time data pipelines using Apache Spark to support large-scale daily data volumes exceeding 10TB and real-time pipelines processing millions of events per minute.
• Build and maintain enterprise data warehouses and data solutions in AWS/GCP cloud environments using big data technologies, ensuring operational stability and optimal performance at petabyte scale.
• Design and develop cloud-agnostic tooling, CI/CD pipelines, serverless compute, and containerized deployments with Kubernetes.
• Provide technical leadership by mentoring and directing engineers and consultants, driving roadmap and sprint planning, and defining long-term strategies for data quality and governance initiatives.
• Collaborate with product managers, data analysts, data scientists, ML engineers, backend engineers, and business stakeholders to translate requirements into scalable data models and data solutions, improve event logging frameworks, enforce data governance standards, and strengthen data foundations for enterprise-wide adoption.
• Influence enterprise-wide technical direction, lead cross-functional efforts to unify fragmented governance practices, drive build-versus-buy decisions, define data asset prioritization frameworks for governance investment, and establish long-term strategies for data engineering, quality, and governance.