Principal Product Manager, EXAScaler

DataDirect Networks (DDN) is a global market leader renowned for powering many of the world's most demanding AI data centers. The Principal Product Manager will lead strategic product areas for EXAScaler, shaping product direction, driving cross-team alignment, and representing the product with senior customers and internal stakeholders.

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

Requirements:

• 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
• 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