Sr. Enterprise Security Engineer

Databricks Databricks · Data AI · CA · Remote · Security

This role focuses on securing enterprise applications, integrations, and data flows, with an emphasis on AI-adjacent use cases, modern access patterns, and cross-system security. The engineer will identify risk, define security requirements, and improve security outcomes through technical judgment and cross-functional partnerships, supporting the secure adoption of SaaS, internal platforms, automation, and AI-connected systems.

What you'd actually do

  1. This role will focus on securing enterprise applications, cross-system integrations, data flows, and emerging AI-adjacent use cases.
  2. The scope includes modern access patterns such as MCP, integration, and trust boundary security, and broader security engineering support across enterprise platforms and services.
  3. This engineer will help identify risk, define practical security requirements, and improve security outcomes through strong technical judgment and cross-functional partnership.
  4. The engineer will review new technologies, integrations, and workflows with an emphasis on secure design, authentication and authorization, data handling, logging, third-party connectivity, API and token security, and operational resilience.
  5. This is a strong opportunity to help shape how Enterprise Security supports SaaS, internal platforms, automation, and AI-connected systems as the environment continues to grow in complexity.

Skills

Required

  • security engineering
  • enterprise security
  • application security
  • cloud security
  • security design reviews
  • architecture reviews
  • enterprise applications
  • SaaS platforms
  • integrations
  • internally developed systems
  • authentication
  • authorization
  • SSO
  • federation
  • SCIM
  • API security
  • token handling
  • secrets management
  • least privilege design
  • data flows
  • third-party integrations
  • trust boundaries
  • logging and monitoring
  • security controls
  • risk evaluation
  • automation platforms
  • AI-adjacent workflows
  • MCP
  • integration patterns
  • written communication skills
  • verbal communication skills
  • technical risk translation
  • clear requirements
  • actionable guidance
  • engineering judgment
  • influence
  • scalable process improvement
  • cloud platforms
  • enterprise identity systems
  • audit logging
  • encryption
  • access control
  • data retention
  • incident response

What the JD emphasized

  • AI-adjacent use cases
  • AI-adjacent security
  • AI-connected systems
  • AI-adjacent workflows