Staff Engineer - Enterprise Data Governance

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About the role

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years, fueled by incredible technology and amazing people. Today, we're using the power of AI to define the next era of computing, with our GPU acting as the brains of computers, robots, and self-driving cars. Doing what's never been done before takes vision, innovation, and the world's best talent. As an NVIDIAN, you'll be immersed in a diverse, encouraging environment where everyone is inspired to do their best work. NVIDIA is hiring a Staff Engineer to build the company's Enterprise Data Governance platform. This platform spans sensitive-information protection, governed data access across every collaboration and content system at NVIDIA, and the knowledge infrastructure that makes our enterprise AI trustworthy and accurate. What makes this role genuinely hard is the tension at its core: AI agents need broad, deep access to enterprise knowledge to be useful, while export controls, legal constraints, and security policy demand precise, auditable access control. Getting both right, across dozens of systems and one of the most complex enterprise environments in the industry, is the problem this team is solving. We are looking for technically strong engineers across the senior to principal level who thrive on hard infrastructure problems and want to own work that is central to how NVIDIA operates at scale. What You Will Be Doing: Sensitive Information Protection • Own the roadmap for sensitive-information detection and remediation, working with Finance, Legal, and Security to define classification models, remediation workflows, and reporting that leadership can trust. • Evaluate and integrate ML-based classification approaches to detect sensitive content across unstructured data at scale, and iterate on precision and recall as the data landscape evolves. Governed Data Access at Scale • Drive production rollout and self-service onboarding for the enterprise data access platform, spanning connectors across email, messaging, document stores, and search (Outlook, Teams, Slack, Confluence, OneDrive, SharePoint, Google Drive, Glean). • Design and implement export-control enforcement and long-term audit logging, including authorization checks, schema design, data masking, retention, and RBAC controls across every connected system. Enterprise AI Knowledge Readiness • Integrate enterprise content sources including document stores, wikis, and cloud drives into the AI knowledge platform, ensuring content is accurate, fresh, and correctly scoped so AI agents only surface what they are authorized to access. • Set and enforce the quality bar for content ingestion across every integration, from freshness and accuracy to access-control correctness. Technical Leadership • Serve as technical lead across all three charters, making architectural calls, resolving ambiguity, and keeping the team focused on what ships. • Raise the engineering bar through code reviews, design reviews, and technical mentorship. What We Need To See: • Bachelor's or Master's Degree in Computer Science, Computer Engineering, or a related field (or equivalent experience). • 8+ years of experience building and operating large-scale enterprise platforms, with demonstrated growth in technical scope and ownership. • Strong foundation in backend systems, distributed systems, and data engineering, including large-scale data processing, indexing pipelines, and systems built for reliability and scale. • Experience building or integrating data connectors or enterprise SaaS integrations (e.g., Confluence, SharePoint, Google Drive, Slack, Teams, or similar). • Experience training and evaluating ML models for classification tasks, particularly in security, content sensit

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