Product Marketing Manager, Cloud Security

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.

Define multi-year AI vision and roadmap for 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) into concrete product requirements and joint go-to-market narratives with Microsoft.

Balance hyperscaler co-development constraints with NetApp differentiation (enterprise data services, multiprotocol access, lifecycle management, resiliency, and cross-cloud consistency where relevant).

Prioritize investments across performance, scale, data services, protocol and API surfaces, and operational excellence for AI pipelines.

Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) Agentic workflows and orchestration (durable shared state, tool/data access patterns—where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Hyperscaler & ecosystem partnership

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.

Market intelligence & evangelism

Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; 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. Prioritize investments across performance, scale, data services, protocol and API surfaces, and operational excellence for AI pipelines.

Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) Agentic workflows and orchestration (durable shared state, tool/data access patterns—where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Hyperscaler & ecosystem partnership

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.

Market intelligence & evangelism

Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; 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. 10+ years product management in cloud infrastructure, enterprise storage, AI/ML infrastructure, or data platforms (principal scope: portfolio strategy, multi-team alignment, executive storytelling). NFS/SMB semantics, snapshots/clones, replication, backup integration patterns, capacity/performance tiers, and large-scale filesystem behavior under parallel workloads. LLMs, RAG architectures, embeddings/vector retrieval patterns, training vs. inference IO profiles, orchestration, and enterprise AI data pipelines. Excellent written and verbal communication to customers, executives, and engineers.

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). Compensation:The target salary range for this position is $228,000 - $325,000. 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.