Senior Product Manager: Extensible Compute

Snowflake•Menlo Park, CA
•$200,000 - $287,500•Hybrid

About The Position

Snowflake is hiring a Senior Product Manager for its Extensible Compute team, which builds Snowpark Container Services (SPCS). SPCS runs customer container workloads and Snowflake first-party products, including Openflow, Notebooks, Streamlit, and AI agents. The role owns the product direction for the platform, including its compute offerings, reliability and cost, and the reusable capabilities that serve partner teams and customers. The role owns the SPCS roadmap and delivery, including the compute instance portfolio across AWS, Azure, and GCP (general-purpose CPU, ARM, and GPU, including new Nvidia generations), along with the storage and file-system capabilities workloads require. The Senior Product Manager turns partner requests into standard platform features where appropriate, guides features from early preview through general availability, and sets launch criteria for security, reliability, and unit economics. The position works closely with engineering on architecture, performance, reliability, and cost; partners with Finance on pricing; and serves as a product contact for infrastructure partners and account teams qualifying strategic customers. AI is expected to be part of the role’s work, including customer and partner synthesis, specifications, launch reviews, and prioritization analysis. The position is hybrid and located in MPK or Bellevue.

Requirements

  • At least 5 years of product management experience, including several years on a platform, infrastructure, or developer-facing product, or equivalent experience owning a technical product end to end.
  • Hands-on depth in at least one of cloud infrastructure, container or serverless platforms, developer runtimes, or distributed systems.
  • Ability to work through design reviews with senior engineers and reach decisions.
  • Track record of taking a platform capability from 0 to 1 or from early adoption to scale across engineering and at least one partner team, such as product, GTM, or a cloud/hardware partner.
  • Demonstrated ability to prioritize reusable capabilities over bespoke requests and explain what was cut and why.
  • Clear writing and communication with engineering leads, partner product managers, Finance, and account teams.
  • Bachelor's degree in a technical field or equivalent experience.

Nice To Haves

  • Direct experience with a container platform, Kubernetes-based service, or serverless compute product used by customers for production workloads.
  • Experience qualifying or launching GPU or accelerated compute instances, or working on a hardware or cloud service provider roadmap involving Nvidia, AWS, Azure, or GCP.
  • Experience supporting data, AI, or developer workloads on a shared platform, such as orchestration, notebooks, apps, or agents.
  • Familiarity with compute or storage unit economics, including instance pricing, per-GiB pricing, or cost-to-serve for a multi-tenant platform.

Responsibilities

  • Own the SPCS product roadmap and ship platform capabilities.
  • Manage the compute instance portfolio across AWS, Azure, and GCP, including general-purpose CPU, ARM, and GPU instances, as well as storage and file-system capabilities.
  • Convert requests from first-party product partners into standard SPCS features when appropriate, distinguishing reusable platform capabilities from one-off solutions.
  • Take features from early preview to general availability; set launch criteria and delay releases when security, reliability, or unit economics are not ready.
  • Work with engineering on architecture, performance, reliability, and cost.
  • Own platform health, including startup time, isolation, storage reliability, and workload unit economics.
  • Partner with Finance on pricing, including per-GiB and instance pricing when needed.
  • Serve as the product point of contact for infrastructure partners AWS, Azure, GCP, and Nvidia on the SPCS compute roadmap, and for account teams qualifying strategic customers.
  • Use AI in customer and partner synthesis, specifications, launch reviews, and prioritization analysis.
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