Pure Storage
Consulting Pre-Sales Systems Engineer, Strategics - NYC
$172K–$258K
New York, New York +2 · Full-time · Posted Oct 28, 2026
- Location
- New York, New York
Newark, New Jersey
New York, United States - Experience
- 10 or more years in enterprise infrastructure, with at least five in a customer-facing presales, solutions architecture or field engineering role. Direct experience covering large financial services institutions is strongly expected.
About the job
Leads technical strategy and complex presales pursuits for major financial institutions, advising on enterprise data platforms, storage modernization, AI infrastructure, and resilience.
What you'll do
- Build technical account plans and serve as a trusted counterpart to customer architects, platform leaders, and executives.
- Lead solution design, sizing, RFI/RFP responses, proofs of concept, evaluations, and competitive bake-offs.
- Coordinate specialists, services, and partners while advising on AI data infrastructure, Kubernetes/OpenShift services, storage, and cyber resilience.
What they ask for
- 10+ years in enterprise infrastructure, including at least 5 years in customer-facing presales, solutions architecture, or field engineering.
- Hands-on command of enterprise block, file, and object storage, data protection, and replication.
- Working fluency in virtualization, Kubernetes and container platforms (particularly OpenShift), storage networking, and at least one major public cloud.
- Strong executive communication and a track record winning technical evaluations and building customer-scrutinized displacement cases.
Nice to have
- Familiarity with analytics and lakehouse platforms, data governance and discovery, and GPU/AI infrastructure and workloads.
- Relevant certifications or experience with Everpure FlashArray, FlashBlade, or Portworx.
Pay
$171,500 — $257,600 USD (base salary)
Tools
Kubernetes, OpenShift, NVMe over TCP, Ethernet, RDMA, Fibre Channel, NVIDIA DGX, GPUDirect Storage
AI focus
Help customers design storage and data layers for AI at scale, including GPU cluster storage, inference and cache offload, retrieval and analytics pipelines, and governed data in place.
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