Principal Product Manager

Veeam is the Data and AI Trust Company, specializing in helping organizations ensure their data and AI are fully understood, secured, and resilient to enable the acceleration of safe AI at scale. As the market leader in both data resilience and data security posture management, Veeam is built for the convergence of identity, data, security, and AI risk. Headquartered in Seattle with offices in more than 30 countries, Veeam protects over 550,000 customers worldwide, who trust Veeam to keep their businesses running. Join us as we go fearlessly forward together, growing, learning, and making a real impact for some of the world’s biggest brands.

Veeam is looking for a high-agency, hands-on Principal Product Manager to help define what data and AI trust looks like in the agentic era. AI is a generational discontinuity: autonomous agents now read, modify, and delete enterprise data at machine speed, and the AI factories behind them increasingly run as stateful, cloud-native workloads on Kubernetes — from AI pipelines and model artifacts to the vector databases powering RAG. The infrastructure to deploy AI exists; the infrastructure to trust it is still being built. As data resilience, data security, and AI governance converge on the Veeam DataAI Command Platform, we need a builder who can take ideas from 0 to 1 at that intersection. You’ll join a friendly, fast-moving team of builders who win together and move fast.

This is a senior individual contributor role — there are no direct reports, and your impact comes from what you build, ship, and influence, not from the size of an org chart. You’ll thrive here if you love building and are energized by ambiguity.

• Bridge data resilience, security, and AI. Connect the dots between data resilience, data security posture, and AI governance — identifying where they converge and defining products that keep customers resilient, secure, and AI-ready.

• Seize the agentic AI opportunity. Shape new use cases for protecting and securing AI workloads — especially in Kubernetes environments — from detecting risky agent activity to undoing AI mistakes with precise, clean, verified recovery.

• Roll up your sleeves with engineering. Work shoulder-to-shoulder with globally distributed development teams on use-case architecture and platform hardening — from authentication and logging to secrets management — and tease out the minimum viable product.

• Champion security and compliance as deal accelerators. Partner cross-functionally to triage security findings into fixable work and drive compliance (e.g., SOC 2, STIG, NIST, VPAT) that unblocks regulated and public-sector deals.

• Own the “why.” Articulate why we are building what we’re building, and bring engineers, customers, and leadership along once the merits are clear.

• Educate customers and the field. Partner with sales and lead customers to articulate and validate the value proposition, build compelling demos and proofs of concept, and evangelize at analyst and community events.

• Innovate in the eyes of the customer. Use what you learn from customers to lead the development roadmap iteratively — ship, learn, and refine in tight loops so every release reflects real-world needs.

• Take initiatives from 0 → 1. Turn ambiguous ideas into validated concepts, prototypes, and first releases — creating new products and capabilities where none existed before, then scaling what works.

• Measure what matters. Define and track success metrics for your product areas, and use them to inform what we build next.

• Strengthen the ecosystem. Enable tighter integration with alliance and ecosystem partners to deliver value to customers faster.

• Expertise across data resilience and security. Hands-on understanding of data protection, backup and recovery, and security disciplines (e.g., ransomware resilience, threat detection, identity and access including OAuth 2.1/OIDC, data security posture, AI security and governance, software supply chain security) — and how they intersect in enterprise environments.

• AI and agentic fluency. A working understanding of how LLMs, RAG pipelines, and AI agents are built and deployed — and the new data, identity, and recovery risks they introduce.

• Cloud and infrastructure fluency. Comfortable setting up and operating your own environments across public cloud (AWS, Azure, GCP) and on-premises infrastructure, including secrets management (e.g., HashiCorp Vault).

• A genuine love for building and technology. A tinkerer at heart who tests, breaks, and learns by doing.

• Relevant background. Prior experience in product management and/or sales engineering; technical roles such as solutions architecture, security engineering, DevOps, or technical marketing engineering are a strong plus.

• First-principles problem solving. Ability to break complex problems into simple, shippable steps and a data-centric mindset that forms convictions through rigorous analysis.

• Influence without authority. Strong communication skills to engage engineers, enterprise customers, and senior leadership with clarity and credibility.

• High agency in fast-moving environments. A self-starter who navigates uncharted waters, manages multiple initiatives at once, and thrives in an agile, non-hierarchical setting rather than a waterfall one.

• A team-first approach. A can-do collaborator who wins as a team, builds alignment across the organization, and executes.

• A track record of 0 → 1 shipping. Proven experience taking new products and capabilities from concept to market success.

• Willingness to travel. Open to travel for customer meetings, partner activities, and conferences.

• Kubernetes and cloud-native knowledge. Familiarity with Kubernetes and the broader cloud-native ecosystem (e.g., Red Hat OpenShift) — especially for AI workloads — including participation in the CNCF / open-source community (e.g., Kanister).

• Hardening and supply chain tooling. Experience with image signing and verification (e.g., Sigstore), reducing third-party dependency and CVE exposure, and modernizing install, logging, and secrets workflows.
• Industry experience.Background in the data protection or cybersecurity industry, including exposure to storage, database, and security ecosystems and their APIs