Principal Product Manager, ExaScaler

Department: Top Level - By Department

Location: Santa Clara

Compensation: $200K – $240K

Employment Type: FullTime

DDN is seeking a Principal Product Manager to lead strategic product areas for EXAScaler. This role shapes product direction, drives cross-team alignment, and represents the product with senior customers and internal stakeholders.

Key Responsibilities

• Define and drive the multi-release strategy and roadmap for major EXAScaler product domains.

• Translate strategy into prioritized, outcome-driven roadmaps, clear PRDs, and detailed user stories with measurable success criteria.

• Partner with engineering leadership to make architecture, investment, and sequencing decisions, balancing innovation with reliability and technical debt reduction.

• Act as a senior product voice with customers and partners, including executive briefings, roadmap deep dives, and joint solution planning for large-scale AI and HPC deployments.

• Shape competitive strategy for EXAScaler in AI and high-performance data infrastructure, including pricing and packaging input, win/loss analysis, and market differentiation.

• Use data (product analytics, customer feedback, and financial performance) to drive portfolio-level decisions and product investment trade-offs.

Qualifications

Must Have

• 12+ years of product management experience in infrastructure, data platforms, storage, HPC environments, or cloud services.

• Proven track record of delivering production features at scale, from definition through launch and iteration.

• Demonstrated ability to write concise PRDs and user stories with clear acceptance criteria and measurable success metrics.

• Experience working closely with sales teams, solutions architects, and customers on proof-of-concepts, roadmap discussions, and product escalations.

• Excellent communication and stakeholder management skills across both technical and non-technical audiences.

• Bachelor’s degree in Computer Science, Engineering, or related field, or equivalent practical experience.

Nice to Have

• Experience with data infrastructure supporting AI workloads, including training and inference pipelines or large-scale unstructured data environments.

• Familiarity with parallel file systems and high-performance storage architectures.

• Background in cloud-native architectures including microservices, containers, observability frameworks, and APIs.

• Strong technical depth in at least one of the following:

  • Distributed storage or parallel file systems (Lustre, object, file, block, or key-value storage)

  • Cloud infrastructure platforms (AWS, Azure, GCP) or Kubernetes-based environments

• Prior experience working in a B2B enterprise infrastructure company or high-growth technology environment.