Principal Engineer - Enterprise Content and AI Data Platform Staff

D8 LooP; Santa Clara (CA); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

On-site work

Employee benefits

Job summary

NVIDIA seeks a Principal Engineer to lead Enterprise Content and AI Data Platform initiatives, shaping governance, security, and scalable content integration for AI knowledge systems.

You will architect RBAC, audit logging, and export-control controls while driving secure data connectors across Confluence, SharePoint, Google Drive, and other enterprise sources.

Qualifications

• Experience delivering production-grade governance and security controls.
• Strong knowledge of RBAC, data retention, and audit requirements.
• Experience integrating enterprise content sources into AI platforms.

Responsibilities

• Define production readiness gates and ownership boundaries.
• Implement export-control enforcement and long-term audit logging.
• Drive integration of enterprise content sources into the AI knowledge platform.
• Build secure API platforms and data connectors for enterprise SaaS.
• Leverage Spark/Databricks for audit logging and RBAC.

Skills

Production gates

Security governance

Audit logging

AWS content integration

Data integration

Databricks / Spark

Tools

Airflow

Databricks

Confluence

SharePoint

Google Drive

Slack

Teams

Job description

Principal Engineer - Enterprise Content and AI Data Platform

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by incredible technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing.

Skills, with evidence

• Define production readiness gates and maintain a clear ownership boundary: this team owns integration correctness and quality, not full platform operations. must have
• Governance & security Background in enterprise security, data governance, or compliance platforms — familiarity with classification, remediation workflows, access control models, and audit requirements is a plus. must have · not practised here
• Design and implement export-control enforcement and long-term audit logging — including authorization checks, schema design, data masking, retention, and RBAC controls. must have
• AWS Drive integration of enterprise content sources — document stores, wikis, and cloud drives — into the AI knowledge platform, ensuring content is accurate, fresh, and access-controlled for agent consumption. not practised here
• Airflow / orchestration Experience building or integrating secure API platforms, data connectors, or enterprise SaaS integrations at scale (e.g., Confluence, SharePoint, Google Drive, Slack, Teams, or similar).
• Spark Hands-on experience with Databricks or similar platforms for audit logging, data governance, and RBAC.