Senior Engineer - Enterprise Data Governance

NVIDIA•Us, CA

About The Position

NVIDIA is hiring a Senior 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. The role is challenging due to the tension between AI agents needing broad access to enterprise knowledge and export controls, legal constraints, and security policy demanding precise, auditable access control. This team is solving the problem of getting both right across dozens of systems and one of the most complex enterprise environments in the industry. 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.

Requirements

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 sensitivity, or information governance domains.
Strong communication skills with the ability to translate complex technical tradeoffs into clear recommendations for non-technical stakeholders.
Comfortable making calls with incomplete information and adjusting quickly as priorities shift.

Nice To Haves

Familiarity with access control models, remediation workflows, and audit requirements in enterprise security or compliance contexts.
Experience with AI/LLM data pipelines, vector stores, or RAG architectures, especially where access control fidelity is a hard requirement.
Hands-on experience with Databricks for audit logging and RBAC, or familiarity with Glean or similar enterprise search/DLP products.
Track record of leading platform migrations, tenant consolidations, or governance modernization at scale.
Experience mentoring engineers or leading cross-functional projects that required alignment across Security, Legal, Finance, and platform teams to ship.

Responsibilities

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.
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.
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.
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.