Principal Product Manager, ExaScaler

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.