Senior Engineering Manager, Data Engineering

CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at www.coreweave.com .

What You’ll Do:

The Data Engineering Team builds and operates the foundational data infrastructure powering analytics, AI, and operational decision-making across CoreWeave. We design resilient data pipelines, scalable lakehouse systems, and high-quality datasets that enable teams across Finance, HR, Operations, and Engineering to move faster and make smarter decisions. Our mission is to make CoreWeave’s data reliable, accessible, and actionable at scale.

About the role:

We are seeking a Senior Engineering Manager to lead CoreWeave’s growing data engineering function, responsible for building and maintaining the company’s petabyte-scale enterprise data lake. This role blends technical depth with people leadership - you’ll lead with empathy and energy, guide engineering execution, ensure operational excellence, and stay hands-on in key parts of our data ecosystem. As a technical manager and bridge-builder, you’ll partner closely with stakeholders across the entire business to translate strategic priorities into scalable data solutions, sustainable delivery practices, and a strong culture of ownership and growth.

Who You Are:

• 8+ years of experience building and operating distributed software systems, including significant data engineering experience.
• 3+ years of experience managing and scaling engineering teams responsible for production systems.
• Deep, hands-on experience with distributed data processing frameworks like Spark and Flink.
• Experience operating reliable systems with defined SLAs/SLOs and reliability targets.
• Strong understanding of failure modes in distributed systems and how to design for fault tolerance and recovery.
• Demonstrated experience setting, executing, and tracking objectives and key results (OKRs) using quantitative signals and operational metrics.
• Track record of hiring, onboarding, and performance-managing engineers in growing organizations.

Preferred:

• Experience operating data platforms with fine-grained access controls and permissions management.
• Familiarity working with regulated datasets including SOX-governed data, GDPR-governed data, and PII.
• Experience running data workloads on Kubernetes.
• Experience with distributed stream processing frameworks like Flink and Kafka.
• Familiarity with lakehouse architectures and table formats such as Iceberg or Delta L