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Principal Data Engineer

Zoom Info Technologies LLC · Remote · Anywhere in the World · posted today (2026-10-29)

Full-TimeRemoteExperience 5+ yrsData & AI

Skills: DevOps and Sysadmin

Apply on Zoom Info Technologies LLC's siteCheck your resume match on CampusPe

Job description

Headquarters: Remote-US ZoomInfo is where careers accelerate. We move fast, think boldly, and empower you to do the best work of your life. You’ll be surrounded by teammates who care deeply, challenge each other, and celebrate wins. With tools that amplify your impact and a culture that backs your ambition, you won’t just contribute. You’ll make things happen fast. We are seeking a highly experienced, hands-on Principal Data Engineer to define and evolve the architecture, engineering standards, and operating model for our enterprise data platform. This role is for a technical leader who excels at complex system design: translating business and analytical needs into scalable, reliable, secure, and maintainable data products. The Principal Data Engineer will lead critical architectural decisions across data ingestion, storage, transformation, orchestration, governance, observability, and consumption, while raising the engineering bar through mentorship, design reviews, and pragmatic best practices. You will work across engineering, analytics, platform, security, and business stakeholders to create systems that make trusted data accessible, timely, and actionable at scale. What You’ll Do Lead Data Architecture and System Design Own the technical vision and architecture for scalable, resilient, and cost-effective data platforms and pipelines. Design end-to-end data systems spanning batch and streaming ingestion, transformation, data modeling, storage, orchestration, serving layers, and consumption patterns. Make and document high-quality architecture decisions, including tradeoffs involving performance, reliability, scalability, latency, security, maintainability, and cost. Define reusable architectural patterns for common data engineering challenges, including CDC, event-driven data flows, schema evolution, slowly changing dimensions, data quality, and multi-tenant or domain-oriented data models. Establish clear boundaries and interfaces between op

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