Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Sr. Security Engineer, Enterprise Security
- Remote from
- USA
- Salary
- USD 134,200–230,800 / yr
- Department
- Cybersecurity
- Employment
- Full Time
- Experience
- Open level
- Published
- Apply before
- 7 Dec 2026
- Listing views
- 39
- Application actions
- 2
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About this role.
RDQ427R608
While candidates in the listed location(s) are encouraged for this role, candidates in other US locations will be considered.
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About the Team
The Enterprise Security team at Databricks is responsible for securing the corporate environment, including the SaaS platforms, internal tools, integrations, automation workflows, and AI-adjacent systems that Databricks employees and operations depend on.
We work at the intersection of enterprise architecture, security engineering, and business enablement, partnering with IT, Engineering, Legal, Privacy, and Procurement to surface risk early, set enforceable security standards, and enable the business to operate securely at scale.
The Role
As a Sr. Security Engineer on the Enterprise Security team, you turn security requirements into working protections across Databricks’ enterprise environment.
You implement, integrate, test, and improve security controls across enterprise applications, identity, endpoint, network, SaaS, and data-security systems. You bring strong hands-on engineering experience, developing automation and reusable capabilities that strengthen preventive controls, improve security coverage, and reduce repetitive manual work.
You work closely with security domain leads, IT, and platform owners to translate approved security designs into practical implementations. Your focus is on delivering reliable controls, automating recurring security checks and workflows, and continuously validating that protections remain effective as enterprise systems evolve.
The Impact You Will Have
Security Control Engineering & Implementation
Implement and improve preventive security controls across enterprise systems, translating approved security requirements into tested, operational protections.
Partner with security domain leads, IT, and platform owners to implement access, configuration, endpoint, network, and data-protection guardrails.
Develop reusable technical implementations and secure configuration patterns that improve consistency and reduce recurring security gaps.
Security Automation & Integrations
Build and maintain security automation using Python, Go, APIs, Terraform, or comparable technologies to improve control enforcement and operational efficiency.
Automate recurring configuration checks, access validation, evidence collection, and remediation workflows to reduce dependency on manual security processes.
Develop and integrate AI-assisted and agent-based security automation to streamline recurring reviews, control validation, and remediation workflows, with appropriate access controls, testing, and auditability.
Integrate security platforms and enterprise systems to streamline established review and approval workflows, surface actionable findings, and support consistent enforcement.
Continuous Control Validation & Remediation
Develop automated testing, monitoring, and validation capabilities to confirm security controls operate as intended.
Identify control gaps, configuration drift, and ineffective enforcement; partner with system owners to implement and validate remediation.
Measure improvements in security coverage, control effectiveness, automation reliability, and reduced manual effort.
Cross-Functional Security Engineering
Partner with Enterprise Security, IT, Engineering, and business-system owners to deliver security improvements across interconnected enterprise platforms.
Troubleshoot complex implementation challenges involving authentication, API integrations, permissions, configurations, and security tooling.
Contribute to Security by Design initiatives by translating recurring security requirements into reusable controls, automated checks, and secure defaults.
What We Look For
7+ years of experience in security engineering, enterprise security, infrastructure security, cloud security, or a related field.
Demonstrated hands-on experience implementing, integrating, testing, and maintaining security controls in production enterprise environments.
Strong programming and automation skills using Python, Go, or comparable languages, including API integrations, infrastructure-as-code, and version control.
Experience engineering security controls across one or more domains, such as identity, endpoint, SaaS, cloud, network, or data protection.
Strong understanding of authentication, authorization, SSO, OAuth, service identities, API security, secrets management, and least-privilege principles.
Experience developing automated security checks, configuration validation, drift detection, and remediation workflows.
Hands-on experience building or integrating AI-assisted or agent-based security automation, including appropriate access controls, testing, monitoring, and auditability.
Ability to translate approved security requirements into preventive controls, troubleshoot implementation challenges, and validate control effectiveness.
Experience applying reliable software engineering practices, including automated testing, CI/CD, error handling, and operational monitoring.
Demonstrated ability to independently deliver security engineering improvements, collaborate with Security, IT, and platform owners, and achieve measurable security outcomes.
Nice to Have
Experience implementing security controls across multiple enterprise security domains rather than specializing in a single area.
Familiarity with enterprise security platforms and technologies such as Okta, Google Workspace, CrowdStrike, Jamf, Prisma Access, or similar solutions.
Experience with specialized policy-as-code frameworks such as Open Policy Agent (OPA), Rego, or comparable enforcement technologies.
Experience securing enterprise data and AI platforms, including Databricks or comparable technologies.
Experience working in large-scale or rapidly growing cloud and SaaS environments.
Pay Range Transparency
Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected base salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipated utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.
Zone 1 Pay Range
$167,800—$230,800 USD
Zone 2 Pay Range
$151,000—$207,700 USD
Zone 3 Pay Range
$142,600—$196,150 USD
Zone 4 Pay Range
$134,200—$184,600 USD
About Databricks
Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn, X, YouTube, and Instagram.
Benefits
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here.
Our Commitment to Diversity and Inclusion
At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.
Compliance
If access to export-controlled technology or source code is required for performance of job duties, it is within Employer’s discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.
This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.
Role DNA
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Pace & Pressure
4/5Autonomy Level
5/5Communication Load
4/5Salary analysis
Estimated compensation compared with the broader US market for similar roles.
Core skills
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Sample interview questions
I would first define the approved authorization model and data sources, then use platform APIs to collect role, group, and entitlement data on a scheduled or event-driven basis. The automation would compare current state with policy, create auditable findings, and route low-risk remediations through approved workflows while requiring approval for sensitive changes. I would add testing, least-privilege service identities, retry handling, monitoring, and metrics for coverage and remediation time.
I would translate the control requirements into repeatable positive and negative tests, such as validating approved identity-provider flows, token audience and scope restrictions, MFA enforcement, and blocked unauthorized paths. These tests should run in CI/CD where possible and continuously in production using safe synthetic identities. I would alert on failures, retain evidence, and coordinate remediation with the application and identity owners.
AI-assisted workflows should have narrowly scoped identities, explicit input and output validation, approval gates for impactful actions, and full audit logs. I would ensure the agent cannot independently expand its permissions or access sensitive data beyond its assigned task. I would also implement deterministic fallbacks, test against unsafe prompts and malformed inputs, monitor actions, and regularly review the workflow's effectiveness and error rate.
I use infrastructure-as-code to codify approved configurations, peer-review changes, and make security controls reproducible across environments. Policy checks can run before deployment to prevent insecure configurations, while post-deployment validation detects drift. Versioned modules and documented secure defaults make it easier for platform teams to adopt controls consistently.
I would prioritize based on asset criticality, exposure, likelihood of exploitation, privilege level, data sensitivity, existing compensating controls, and remediation effort. I would work with system owners to distinguish urgent preventive fixes from longer-term engineering improvements. Clear metrics, such as affected users, time-to-remediate, control coverage, and recurrence rate, would help communicate priorities and demonstrate progress.
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