Principal Product Manager, Veeam

• 10+ years in product management, including 4+ years leading product managers or owning a major platform area, with a track record shipping technical/platform products (data platforms, security or governance tooling, analytics, or developer-facing systems)
• Strong data fluency: comfortable with data models, SQL, and graph concepts (nodes, edges, relationships, lineage), able to reason about queries and their cost, and able to partner credibly with senior engineers on architecture, performance, and scale trade-offs without needing to be the architect
• Track record owning a reporting or analytics product area (dashboards, report builders, scheduling, exports) that non-technical enterprise users actually adopt, plus experience building composable, widget-based dashboards with user-configurable layouts that turn insights into in-product actions and workflows rather than read-only charts
• Judgment about dashboard design: what belongs on a default view, how to make a metric drillable, and how to keep a dashboard useful as data volume grows, along with experience making reports credible at enterprise scale (accuracy, freshness, disclosed coverage gaps, performance on large datasets)
• Experience with enterprise security, privacy, or data governance buyers (CISO, DPO, data governance, audit) and the evidence they need
• Excellent written communication: crisp specs, clear positioning, and roadmaps that survive scrutiny
• Hands-on use of AI-assisted tools in your own workflow, and a view on how AI should change the product itself

Desirable

• You will own product for the Knowledge Graph and the reporting and analytics experience inside Veeam Data Command Platform (DSPM and AISPM). The graph is where the product's intelligence lives: what sensitive data exists, who can access it, how it moves, and whether the AI working on it can be governed. Actionable dashboards and reporting are how customers see that intelligence, prove their posture to auditors and executives, and decide — and act on — what to fix first
• This is a builder's product role. You will translate a complex graph data model into queries, views, dashboards, and evidence that security, privacy, and governance teams use every day — and you will partner closely with the engineering leader who owns the graph platform and hyperscale lake integrations
• You will set the roadmap, define the metrics that matter, and work AI-first — using agentic tools to prototype, analyze, and ship faster than a traditional PM team
• Own the product vision and roadmap for the Knowledge Graph as a customer-facing capability: the entities, relationships, and insights customers can explore, query, and trust. Partner with engineering on the underlying data modeling (sources, identities, access, classifications, lineage) and translate those decisions into concepts customers actually understand
• Own the dashboard experience end to end: default landing views per persona (CISO, privacy, data governance, security operations), what earns a spot on them, and the path from a top-line number to underlying records in a few clicks. Every widget should answer "so what do I do now?", letting a user assign an owner, open a ticket, launch remediation, tighten a permission, or apply a policy without leaving the view
• Own the widget framework and library (posture scores, top risks, sensitive-data heatmaps, access exposure, AI usage, trend lines) with consistent filtering, time ranges, and drill-through. Enable customization and role-based layouts so customers and internal teams can build, save, share, and default dashboards by role or business unit
• Define the reporting layer: a report builder with saved/shared views, scheduling and subscriptions, and export in the formats enterprises use (PDF, CSV, API). Own audit and evidence packages that hold up for regulators and auditors, with clear scope, timestamps, and provenance back to the graph, and extend reporting coverage to backup and archive estates (Veeam Data Cloud workloads: Microsoft 365, Entra ID, Salesforce) so posture and evidence reflect historical data, not just production
• Drive trending and posture-over-time reporting, including alerting and thresholds, so customers can show risk reduction rather than a single snapshot. Set the bar for data quality and freshness: how recent the numbers are, how coverage gaps are disclosed, and how the product avoids reporting false confidence
• Enable downstream and agent consumption: BI integration, warehouse export, and APIs for customers who want the data in their own stack, plus partnering with the AI Agents team to expose graph queries, findings, and posture metrics as MCP tools and resources for customer and internal AI agents
• Turn graph traversals into product surfaces: access paths, blast radius, data flow/lineage views, AI exposure, and risk prioritization. Partner with the Access Entitlements team on identity-to-data surfaces (multi-hop reachability through nested groups and shadow roles, least-privilege recommendations, entitlements for non-human identities). Own query and search UX for non-technical users, including natural-language and AI-assisted question answering over the graph, and define scale/performance requirements from the user's point of view (what must return in under a second, what can be async, where multi-hop queries must stay interactive at enterprise scale), holding the bar with engineering
• Build the metrics and KPI framework the platform reports on, aligned with regulatory and framework mappings customers are measured against. Run discovery with enterprise CISOs, DPOs, and data governance leads and design partners, converting findings into a prioritized, defensible roadmap
• Work AI-first, using agentic tools (Claude Code, Cursor, Copilot) and reusable Claude skills to prototype, query data, draft specs, and validate designs. Partner with design, engineering, PMM, and support to land releases: positioning, documentation, enablement, and adoption tracking