Principal Product Manager

NetApp, Inc.•Waltham, MA

3d•$228,000 - $325,000

About The Position

NetApp is hiring a principal-level product leader to own the AI product strategy for Azure NetApp Files (ANF)—a first-party, fully managed enterprise file service on Microsoft Azure, delivered in deep partnership between NetApp and Microsoft. In the spirit of NetApp’s “business builder” cloud roles, you will translate a fast-moving AI landscape into differentiated platform capabilities, joint roadmap bets with Microsoft, and enterprise outcomes (performance, data locality, governance, and time-to-value for AI pipelines). You will sit at the intersection of enterprise storage, Azure AI infrastructure, and industry AI workloads, ensuring ANF is positioned and built as a strategic data foundation for training, inference, RAG, analytics, simulation, and agentic workflows—without forcing customers to abandon enterprise file semantics, protection, or hybrid operating models.

Requirements

10+ years product management in cloud infrastructure, enterprise storage, AI/ML infrastructure, or data platforms (principal scope: portfolio strategy, multi-team alignment, executive storytelling).
Strong command of enterprise storage: NFS/SMB semantics, snapshots/clones, replication, backup integration patterns, capacity/performance tiers, and large-scale filesystem behavior under parallel workloads.
Hands-on familiarity with modern AI stacks: LLMs, RAG architectures, embeddings/vector retrieval patterns, training vs. inference IO profiles, orchestration, and enterprise AI data pipelines.
Demonstrated success influencing engineering and partner roadmaps without direct authority; experience with hyperscaler first-party or deeply partnered services is a strong plus.
Excellent written and verbal communication to customers, executives, and engineers.

Nice To Haves

Direct experience with Microsoft Azure AI services, GPU estates on Azure, and/or Azure Kubernetes Service + ML platform integrations.
Familiarity with Databricks, Iceberg/Delta-class open table patterns, Kubernetes storage patterns, NVIDIA AI software stacks, and enterprise MLOps release cadences.
Background in regulated industries and enterprise security/governance requirements for AI data.
MBA or advanced degree in CS/Engineering (helpful, not a substitute for demonstrated technical depth).

Responsibilities

Own end-to-end AI strategy for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options.
Prioritize investments across performance, scale, data services, protocol and API surfaces, and operational excellence for AI pipelines.
Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O), RAG and enterprise search (datasets, versioning, clones, refresh patterns), Agentic workflows and orchestration (durable shared state, tool/data access patterns—where productized responsibly), Large multimodal and enterprise datasets (governance, access control, lifecycle), Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics).
Partner with Microsoft teams across Azure AI / Foundry, Azure Machine Learning, AKS / container platforms, GPU infrastructure, data/analytics (e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies.
Align ANF’s AI story with Azure-wide AI data guidance and reference architectures, and feed real customer workload evidence back into joint planning.
Lead across engineering, product marketing, sales, customer success, and professional services to ship capabilities and repeatable reference architectures / proof points.
Engage strategic customers and design partners to validate pain, quantify value, and de-risk roadmap bets.
Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into differentiated bets.
Represent ANF as a credible technical executive in briefings, advisory councils, and industry forums.
Tailor AI storage strategy for segments where file semantics and performance matter, for example: semiconductor/EDA, manufacturing, healthcare imaging, financial services, energy, media & entertainment, and HPC/simulation—including compliance and data residency realities.