Staff Enterprise Security Engineer

RDQ227R1176

While candidates in the listed location(s) are encouraged for this role, candidates in other locations (US based) will be considered.

Mission

Databricks is hiring an L6 Staff Enterprise Security Engineer to expand Enterprise Security coverage across a rapidly evolving enterprise and product environment. This role will focus on securing enterprise applications, cross-system integrations, data flows, and emerging AI-adjacent use cases. The scope includes partnership with the Data team on corporate production development within the Databricks platform, through security reviews of implementation designs, hardening, configuration oversight, and broader enterprise data-security discussions, as well as modern access patterns such as MCP, integration, and trust boundary security, security automation, and broader security engineering support across enterprise platforms and services. This engineer will help identify risk, define practical security requirements, and improve security outcomes through strong technical judgment, hands-on engineering, and cross-functional partnership.

Opportunity

This role sits at the intersection of enterprise architecture, security engineering, product security partnership, and business enablement. Corporate production development within the Databricks platform remains owned by the Data team; EntSec defines how we engage, reviewing implementation designs, configurations, and data flows as capabilities are built, alongside ongoing enterprise application and integration reviews. The engineer will assess 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. The role partners closely with Product Engineering, IT, Legal Privacy, and business stakeholders to surface risk early, set clear requirements, and build automation that scales security coverage. This is a strong opportunity to help shape how Enterprise Security supports Databricks product development, enterprise data security, SaaS, and internal platforms, automation, and AI-connected systems as the environment continues to grow in complexity.

Requirements

8+ years of experience in security engineering, enterprise security, application security, cloud security, or a related field.

Experience conducting security design or architecture reviews for product features, enterprise applications, SaaS platforms, integrations, or internally developed systems.

Hands-on familiarity with the Databricks platform or comparable data/AI platforms (Unity Catalog, workspace governance, service principals, data access patterns).

Strong understanding of authentication, authorization, SSO, federation, SCIM, API security, token handling, secrets management, and least privilege design.

Experience assessing data flows, third-party integrations, trust boundaries, logging and monitoring, and security controls across interconnected systems.

Proven track record building security automation in production: monitoring, posture checks, review workflows, or tooling (Python, SQL, Terraform, or similar).

Ability to evaluate risk in modern enterprise environments, including automation platforms, AI-adjacent workflows, and emerging integration patterns such as MCP.

Strong written and verbal communication skills, including the ability to translate technical risk into clear requirements and actionable guidance.

Experience driving security outcomes through engineering judgment, influence, and scalable process improvement.

Familiarity with cloud platforms, enterprise identity systems, and core control domains such as audit logging, encryption, access control, data retention, and incident response.

Outcomes

OUTCOME 1: Establish a consistent EntSec engagement model with the Data team on corporate production development within the Databricks platform: security review of implementation designs, hardening guidance, configuration oversight, and tracked remediation, while Data retains ownership of product development and delivery.

OUTCOME 2: Strengthen security practices across enterprise applications, integration, and data-security reviews by identifying key risks early, improving requirement quality, and helping teams address security issues earlier in the lifecycle, including AI-adjacent workflows, data flows, and integration patterns.

OUTCOME 3: Build automation and agent capabilities, including SSPM-style controls and Security AI Personas, that help secure systems from the start, reduce dependency on manual review, and embed security guidance earlier in product and integration lifecycles.

Competencies

COMPETENCY 1: Product and Design Security Partnership. Partners effectively with product and engineering teams to review implementation designs, surface risk early, and drive practical security requirements without owning product delivery.

COMPETENCY 2: Data Security and Technical Judgment. Applies strong security judgment to data flows, platform configurations, access patterns, and enterprise data-security decisions on and across the Databricks platform.