Staff Product Manager, Data Services

About the role

The Data Services Team builds and operates the data platform that powers CoreWeave's AI cloud. They handle transactional and analytical storage, streaming, metadata, and governance, providing the necessary data primitives for customers and CoreWeave engineers.

Responsibilities

• Owning the end-to-end strategy, roadmap, and execution for CoreWeave's data services portfolio, including databases, data lakes, streaming, metadata and catalog, and data sharing and integration.
• Shaping a multi-year vision for how our data services underpin AI and GPU workloads, and leading delivery from 0 to 1 and 1 to N.
• Engaging directly with strategic customers and partners, collaborating closely with engineering and GTM, evaluating emerging data technologies, and ensuring our services are secure, reliable, and easy to adopt.
• Tackling the hardest problems around scale, throughput, and performance for GPU-intensive applications, and having real influence over the architecture to solve them.

Requirements

8+ years of product management experience in data platforms, databases, analytics, or cloud infrastructure, with at least 3-4 years focused on data services (managed databases, streaming, data warehouses, data lakes, or governance platforms).

• Technical fluency in relational and/or distributed databases (e.g., PostgreSQL, MySQL, SingleStore, Snowflake, BigQuery)
• Streaming and pipelines (e.g., Kafka, CDC, ETL/ELT, lakehouse architectures, real-time data sharing)
• Data security and governance (encryption, IAM/RBAC, data masking, network isolation, compliance)

Qualifications

• Strong communication skills - able to create clarity from ambiguity, influence without authority, and translate technical depth for any audience
• Comfort operating in a fast-paced, high-growth environment where the answer isn't always obvious
• Staff or Director-level PM experience at a database vendor, cloud provider, or high-growth infrastructure company
• Hands-on experience with managed data services on AWS, Azure, or GCP (e.g., RDS, Aurora, Cosmos DB, BigQuery)
• Background in hybrid or multi-cloud architectures: connectivity, data locality, replication
• Experience with AI/ML data products: feature stores, vector search, real-time analytics, or serving pipelines for LLM workloads
• Familiarity with SaaS/PaaS packaging and usage-based or tiered pricing models
• Prior engineering or architecture experience on data or distributed systems