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 Services Team builds and operates the data platform powering CoreWeave's AI cloud, from transactional and analytical databases to streaming, metadata, and governance. We give customers and CoreWeave engineers the primitives to ingest, store, process, and serve data with the latency, throughput, and elasticity that AI training, inference, and agentic workloads demand.

If you want to shape how data powers a rapidly scaling AI cloud, and build infrastructure that AI applications depend on, this is your team.

About the role

As a Staff Product Manager for Data Services, you'll own strategy, roadmap, and execution across CoreWeave's data services portfolio, with particular emphasis on transactional (OLTP) and analytical (OLAP) platforms, alongside streaming, data lakes, and integration.

You'll shape a multi-year vision for how our services support AI training, inference, and agentic applications, then lead delivery from 0 to 1 and beyond. You'll work directly with customers and engineering to identify opportunities, prioritize investments, differentiate our offerings, and deliver reliable, scalable data infrastructure.

The hardest challenges involve rapid provisioning, data access, throughput, and performance for GPU-intensive and agentic workloads, with meaningful influence over technical direction and customer outcomes.

In this role, you will own

Product strategy and roadmap across transactional and analytical Data Services, balancing immediate customer needs with broader portfolio investments
Customer and partner engagement, understanding data architectures, validating opportunities, and translating requirements into differentiated services and solutions
Cross-functional collaboration with engineering, GTM, and leadership to prioritize investments, resolve tradeoffs, and accelerate delivery
Market and competitive analysis to evaluate alternatives, identify customer value, and justify product investments through measurable business outcomes
API-first platform thinking, partnering with architects on scalability, reliability, service interfaces, and performance for AI workloads

Who You Are

8+ years of product management experience in data platforms or cloud infrastructure, including 3-4 years owning customer-facing managed databases, analytical engines, or data services
Technical fluency in:
Transactional and/or analytical databases (e.g., PostgreSQL, MySQL, Aurora, Snowflake, BigQuery), including workload requirements, performance, and scalability
Streaming and pipelines (e.g., Kafka, CDC, ETL/ELT, lakehouse architectures, data ingestion and processing)
Data security and governance (encryption, IAM/RBAC, tenant isolation, data locality, compliance)
Demonstrated ownership of external, API-first Data Services from concept through launch, adoption, and multiple release cycles
Strong product judgment, translating customer needs into differentiated capabilities, strategic priorities, and measurable business outcomes
Comfort navigating ambiguity, making technical and commercial tradeoffs, and executing quickly in evolving AI infrastructure markets

Preferred

Staff or Principal PM experience at a database vendor, cloud provider, or high-growth infrastructure company
Direct ownership of managed transactional or analytical services on AWS, Azure, or GCP (e.g., RDS, Aurora, Cosmos DB, BigQuery)
Experience with distributed database architectures, replication, workload isolation, elastic scaling, and performance optimization
Experience supporting AI training, inference, or agentic workloads through vector search, rapid provisioning, analytical processing, or data serving