Principal Data Engineer

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.

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 operational systems, data platforms, analytics layers, and downstream applications.

Lead technical design reviews for high-impact initiatives and provide clear, constructive direction on implementation approaches.

Identify architectural risks early and drive plans to mitigate technical debt, single points of failure, data inconsistency, and operational fragility.

Set Engineering Standards and Best Practices

Establish and continuously improve standards for data engineering development, testing, deployment, documentation, monitoring, incident response, and lifecycle management.

Define best practices for version-controlled data transformations, infrastructure as code, CI/CD, code review, automated testing, and environment promotion.

Build a culture of data reliability by implementing standards for data contracts, schema management, lineage, observability, freshness, completeness, accuracy, and reconciliation.

Champion security, privacy, access control, governance, retention, and compliance requirements throughout the data lifecycle.

Develop practical engineering guardrails that enable teams to move quickly without sacrificing quality, trust, or operational excellence.

Drive adoption of well-defined SLIs, SLOs, alerting, runbooks, and post-incident learning practices for critical data products.

Drive Technical Decision-Making

Serve as a trusted technical authority for complex, cross-functional decisions involving data architecture, tooling, platform capabilities, and engineering investments.

Facilitate technical discussions that turn ambiguous requirements into durable, well-reasoned solutions.