Staff Product Manager (Data Services)

The Data Services Team builds and operates the data platform that powers CoreWeave’s AI cloud, from transactional and analytical storage to streaming, metadata, and governance. We give our customers and CoreWeave engineers the primitives they need to ingest, store, process, and serve data at the scale and performance that next-generation AI workloads demand

If you want to define how data flows across a rapidly scaling AI cloud, and turn that strategy into infrastructure others depend on, this is your team

As a Staff Product Manager for Data Services, you’ll own the end-to-end strategy, roadmap, and execution for CoreWeave’s data services portfolio: databases, data lakes, streaming, metadata and catalog, and data sharing and integration

You’ll shape a multi-year vision for how our data services underpin AI and GPU workloads, then lead delivery from 0 to 1 and 1 toN. That means working 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

The hardest problems you’ll tackle are around scale, throughput, and performance for GPU-intensive applications, and you’ll have real influence over the architecture to solve them

Product strategy and roadmap across the data services portfolio, balancing near-term delivery with long-term bets

Customer and partner engagement - diving deep into data architectures, co-designing solutions, and supporting large-scale deployments

Cross-functional alignment with engineering, GTM, and leadership to drive clarity and execution in a fast-moving environment

Market and competitive analysis to surface opportunities, validate priorities, and keep us ahead of the curve

API-first platform thinking - partnering with architects on service designs, SLAs, and operational excellence- 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)

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)

Strong communication skills - able to create clarity from ambiguity, influence without authority, and translate technical depth for any audience

Technical fluency in:

Comfort operating in a fast-paced, high-growth environment where the answer isn’t always obvious

Relational and/or distributed databases (e.g., PostgreSQL, MySQL, SingleStore, Snowflake, BigQuery)

A track record of owning complex, API-first platform products from concept through multiple release cycles

Wondering if you’re a good fit? We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren’t a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk

Love building platforms that other teams and customers treat as critical infrastructure

Can drive clarity in highly technical, ambiguous problem spaces and rally cross-functional teams around a plan

Balance long-term vision with fast iteration, and you don’t need perfect information to move forward

Are curious about new databases, analytics, and AI data patterns, and are good at separating real value from hype

Enjoy going deep with customers on their data architectures and co-designing solutions

Prior engineering or architecture experience on data or distributed systems

Staff or Director-level PM experience at a database vendor, cloud provider, or high-growth infrastructure company

Familiarity with SaaS/PaaS packaging and usage-based or tiered pricing models

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