Principal Product Manager

Job Summary

NetApp is hiring aprincipal-level product leaderto own theAI product strategyforAzure NetApp Files (ANF)-afirst-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 intodifferentiated platform capabilities,joint roadmap betswith Microsoft, andenterprise outcomes(performance, data locality, governance, and time-to-value for AI pipelines).

You will sit at the intersection ofenterprise storage,Azure AI infrastructure, andindustry AI workloads, ensuring ANF is positioned and built as astrategic data foundationfor training, inference, RAG, analytics, simulation, and agentic workflows-without forcing customers to abandon enterprise file semantics, protection, or hybrid operating models.

Role Overview

We need ahighly strategic and deeply technicalprincipal PM who can:

• Definemulti-year AI vision and roadmapfor ANF in the context of Azure AI services, GPU estates, data platforms, and regulated enterprise environments.

• Turn emerging patterns (LLMs, RAG, agents, orchestration, multimodal data, vector retrieval, high-throughput checkpointing) intoconcrete product requirementsandjoint go-to-marketnarratives with Microsoft.

• Balancehyperscaler co-developmentconstraints withNetApp differentiation(enterprise data services, multiprotocol access, lifecycle management, resiliency, and cross-cloud consistency where relevant).

Responsibilities

AI strategy & roadmap

• Own end-to-endAI strategyfor ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options.
• Prioritize investments acrossperformance,scale,data services,protocol and API surfaces, andoperational excellencefor AI pipelines.

Workload-led product definition

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)

Hyperscaler & ecosystem partnership

• Partner withMicrosoftteams acrossAzure 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 withAzure-wide AI dataguidance and reference architectures, and feedreal customer workload evidenceback into joint planning.

Cross-functional leadership

• Lead acrossengineering, product marketing, sales, customer success, and professional servicesto ship capabilities and repeatablereference architectures / proof points.
• Engagestrategic customersanddesign partnersto validate pain, quantify value, and de-risk roadmap bets.

Market intelligence & evangelism

• Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate intodifferentiated bets.
• Represent ANF as acredible technical executivein briefings, advisory councils, and industry forums.

Industry segmentation

• Tailor AI storage strategy for segments where file semantics and performance matter, for example:semiconductor/EDA,manufacturing,healthcare imaging,financial services,energy,media & entertainment, andHPC/simulation-including compliance and data residency realities.

AI strategy & roadmap

• Own end-to-endAI strategyfor ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options.
• Prioritize investments acrossperformance,scale,data services,protocol and API surfaces, andoperational excellencefor AI pipelines.

Workload-led product definition

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)

Hyperscaler & ecosystem partnership

• Partner withMicrosoftteams acrossAzure 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 withAzure-wide AI dataguidance and reference architectures, and feedreal customer workload evidenceback into joint planning.

Cross-functional leadership

• Lead acrossengineering, product marketing, sales, customer success, and professional servicesto ship capabilities and repeatablereference architectures / proof points.
• Engagestrategic customersanddesign partnersto validate pain, quantify value, and de-risk roadmap bets.

Market intelligence & evangelism

• Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate intodifferentiated bets.
• Represent ANF as acredible technical executivein briefings, advisory councils, and industry forums.

Industry segmentation

• Tailor AI storage strategy for segments where file semantics and performance matter, for example:semiconductor/EDA,manufacturing,healthcare imaging,financial services,energy,media & entertainment, andHPC/simulation-including compliance and data residency realities.

Job Requirements

Required

• 10+ yearsproduct management incloud infrastructure,enterprise storage,AI/ML infrastructure, ordata platforms(principal scope: portfolio strategy, multi-team alignment, executive storytelling).
• Strong command ofenterprise storage: NFS/SMB semantics, snapshots/clones, replication, backup integration patterns, capacity/performance tiers, and large-scale filesystem behavior under parallel workloads.
• Hands-on familiarity withmodern AI stacks: LLMs, RAG architectures, embeddings/vector retrieval patterns, training vs. inference IO profiles, orchestration, and enterprise AI data pipelines.
• Demonstrated success influencingengineering and partner roadmapswithout direct authority; experience withhyperscaler first-partyor deeply partnered services is a strong plus.
• Excellent written and verbal communication tocustomers, executives, and engineers.

Preferred

• Direct experience withMicrosoft AzureAI services,GPUestates on Azure, and/orAzure Kubernetes Service+ ML platform integrations.
• Familiarity withDatabricks,Iceberg/Delta-class open table patterns,Kubernetesstorage patterns,NVIDIA AIsoftware stacks, andenterprise MLOpsrelease cadences.
• Background inregulated industriesand enterprise security/governance requirements for AI data.

Education

• MBAor advanced degree inCS/Engineering(helpful, not a substitute for demonstrated technical depth).

Compensation:

The target salary range for this position is $228,000 - $325,000. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU's), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process.

At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person expectations, which will be shared during the recruitment process.

Equal Opportunity Employer:

NetApp is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all federal, state and local laws that prohibit employment discrimination based on age, race, color, gender, sexual orientation, gender identity, national origin, religion, disability or genetic information, pregnancy, protected veteran status, and any other protected classification.

Why You'll Thrive at NetApp

At NetApp, you won't wait for the perfect moment-you'll make it. The early planning, the extra thought, the bold idea that turns good into great: That's how our people operate and how we continue to push the boundaries of data infrastructure.

NetApp is the trusted partner for organizations transforming data into opportunity. As the only enterprise-grade storage service natively embedded in Google Cloud, AWS, and Microsoft Azure, we empower customers to run everything from traditional workloads to enterprise AI with unmatched performance, resilience, and security.

Our culture

We celebrate mold breakers, bold thinkers, and problem solvers. We reward initiative, impact, and ownership. We provide flexibility so you can balance professional ambition with your personal life. Here, differences are not just welcomed-they drive everything we do.

If you're ready to innovate, rise to the challenge, and own every moment - make your next move your best one. Apply now.

Submitting an application

To ensure a streamlined and fair hiring process for all candidates, our team only reviews applications submitted through our company website. This practice allows us to track, assess, and respond to applicants efficiently. Emailing our employees, recruiters, or Human Resources personnel directly will not influence your application.