Sr. Product Manager

NetApp, Inc.•San Jose, CA

1d•$196,350 - $292,600

About The Position

Instaclustr, part of NetApp, is expanding into data infrastructure for AI applications and agents. We are looking for a Senior Product Manager to define and deliver that product: the streaming, retrieval, memory and data-access capabilities that AI developers need, built on open-source engines and running in any cloud or on-premises. You will own the product strategy and roadmap for AI workloads across the Instaclustr platform, working closely with engineering, product marketing, sales and NetApp's AI and storage teams. This is a hands-on role for someone who understands how AI applications use data and can turn that understanding into products developers adopt. As part of NetApp, Instaclustr helps companies run open-source technologies such as Apache Cassandra, Apache Kafka, PostgreSQL, ClickHouse, OpenSearch and Cadence at scale and with confidence. Our platform handles everything from provisioning to production reliability, so customers can focus on building great applications. By combining proven platform-based delivery, open-source innovation, and NetApp's enterprise storage and data services, we are positioned to lead in hyperscaler-agnostic data infrastructure for agentic AI.

Requirements

8+ years of product management experience, including developer-facing products or data infrastructure such as databases, streaming, search or cloud platforms.
Hands-on understanding of how AI applications use data: RAG, vector and hybrid search, embeddings, agent frameworks and memory, and emerging agent protocols such as the Model Context Protocol (MCP).
A track record of shipping technical products from discovery to launch, with measurable adoption.
Ability to go deep with engineers and explain simply to executives and customers; strong writing skills, including working-backwards or product requirements documents.
Experience aligning multiple teams on priorities in demanding stakeholder environments, including across organizational boundaries.
Strong business judgment, including pricing, packaging and the economics of a managed service.

Nice To Haves

Experience with the technologies Instaclustr offers (Apache Cassandra, Apache Kafka, OpenSearch, PostgreSQL, ClickHouse, Cadence) or other open-source data projects and communities.
Experience with managed cloud services, customer-cloud (BYOC) or multicloud deployment, and cloud marketplaces.
Familiarity with enterprise storage and data management, or with data governance and security for AI.
Experience with open table formats such as Apache Iceberg, or lakehouse architectures.
Experience building AI applications yourself, even as prototypes.

Responsibilities

Understand the problem. Lead customer discovery with AI developers, platform engineers and data teams. Learn how RAG, agentic and real-time AI applications use streaming, search, databases and memory, and where they struggle. Size and prioritize the opportunities.
Own the vision and roadmap. Set the product strategy for AI workloads across the platform. Write working-backwards documents that align product, engineering, go-to-market and executives around a clear value proposition.
Define and ship cross-service capabilities. Examples include governed data access for AI agents, AI solution blueprints and reference architectures, open data formats, and integrations with AI frameworks and model providers. Write requirements, make trade-offs, and work with engineering from discovery to launch.
Build with NetApp. Partner with NetApp's AI, storage and data services teams to deliver integrated solutions for NetApp customers, while keeping the platform open and usable on any infrastructure.
Learn from early customers. Run design-partner and early-access programs, and turn what you learn into roadmap decisions.
Partner with product marketing and sales. Bring product and market expertise to positioning, sales plays, enablement and launches. Join strategic customer conversations and represent the product at conferences and in open-source communities.
Measure what matters. Define adoption, usage and revenue metrics for AI workloads, report progress, and adjust course.
Make the hard calls. Act as the decision and escalation point for AI-segment priorities, whether for a strategic customer or a cross-team trade-off, and explain the reasoning clearly.