Presales Storage Architect - Unstructured Data

Presales Storage Architect - Unstructured Data This role has been designed as 'Hybrid' with a requirement that you will work on average 2 days per week from an HPE office. Who We Are: Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work.

We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today's complex world.

Our culture thrives on finding new and better ways to accelerate what's next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good.

If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE. Job Description: We're seeking an experienced Storage Architect with strong expertise in unstructured data, AI and analytics workloads, and modern file and object storage architectures.

This is a highly visible, customer-facing role with significant influence on solution design, customer adoption, and product strategy. Role Overview: As a Storage Architect - Unstructured Data, AI & Analytics,

you will

Partner with customers to architect scalable file and S3 object storage solutions for AI, analytics, and enterprise workloads. Understand application data flows, data lifecycle requirements, and performance characteristics to recommend the right storage architecture.

Size storage solutions based on capacity, throughput, latency, network bandwidth, growth, availability, and workload requirements. Lead technical workshops, assessments, benchmarks, and hands-on proofs of concept (POCs).

Provide strong subject matter expertise in S3 object storage and foundational knowledge of NFS and SMB. Apply knowledge of AI inference, RAG, data lakes, and analytics pipelines to storage architecture and design decisions.

Collaborate with product management and engineering to translate customer requirements into product and roadmap priorities. Bring competitive knowledge and market awareness to customer discussions and internal strategy. Travel as needed to support customers, partners, and strategic opportunities.

Key Responsibilities

Architect and document enterprise-scale file and object storage solutions. Analyze customer data flows, workloads, growth patterns, and data management requirements. Size storage environments for performance, capacity, resiliency, and scalability.

Lead and execute hands-on POCs validating performance, integration, scalability, and operational requirements. Design storage architectures for AI inference, RAG, analytics, and data lake workloads, with consideration for throughput, latency, metadata, network design, and data accessibility.

Help customers understand how data is ingested, stored, processed, protected, moved, governed, and consumed across modern data pipelines. Translate customer requirements and field experience into actionable product feedback and roadmap recommendations.

Provide competitive analysis, technical positioning, and solution guidance to sales and technical teams. Present architectures and technical recommendations to both executive and deeply technical audiences. Support strategic presales engagements, RFIs/RFPs, solution sizing, and technical justification. Required

Qualifications

7+ years of experience designing, implementing, or supporting enterprise storage and data architectures. Bachelor's degree in Computer Science, Information Technology, Engineering, or related field, or equivalent practical experience.

Strong customer-facing architecture, consulting, or presales experience. Strong foundational knowledge of S3 object storage, including buckets, objects, APIs, authentication, data protection, and common application integration patterns.

Working knowledge of enterprise file protocols, particularly NFS and SMB. Experience sizing storage solutions based on capacity, performance, growth, network, and application requirements. Demonstrated ability to plan, configure, execute, and troubleshoot technical POCs.

Understanding of modern AI and analytics data pipelines, including AI inference, RAG, vector databases, data lakes/Lakehouse's, and large-scale analytics workloads. Ability to discuss storage within the broader context of data flow, data lifecycle, data management, protection, governance, and application consumption.

Strong understanding of storage performance concepts including throughput, latency, IOPS, metadata performance, concurrency, and network

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