Senior SaaS Security Engineer
New
A
AppOmniSaaS Security
Remote - USAFull-TimeSenior
Salary175,000 - 200,000 USD per year
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Job Details
- Experience
- 5–8+ years
- Required Skills
- PythonSQLOAuth
Requirements
- 5–8+ years of experience in cybersecurity, with hands-on work in areas such as detection engineering, threat research, security analytics, or cloud/SaaS security.
- Strong understanding of SaaS security concepts, including identity and access management, OAuth integrations, third-party app risks, and misconfiguration-driven exposure.
- Experience working with security telemetry and logs, including querying and analyzing large datasets (e.g., SQL, Python, or similar tools).
- Experience developing or tuning detection logic, rules, or analytics in a SIEM, XDR, or similar system.
- Familiarity with SaaS application APIs and security-relevant data sources.
- Understanding of attacker techniques in SaaS environments, including identity-based attacks, privilege escalation, and persistence mechanisms.
- Ability to translate complex technical findings into clear, actionable security insights.
- Experience balancing detection fidelity, coverage, and performance in production systems.
- Experience partnering with Product and Engineering to deliver customer-facing security capabilities.
Responsibilities
- Design and develop detection logic and security rules to identify threats, suspicious behaviors, and misconfigurations across SaaS applications.
- Research SaaS platforms (e.g., Google Workspace, Microsoft 365, Salesforce, Slack, etc.) to understand security models, APIs, and potential attack surfaces.
- Translate real-world attack techniques and SaaS security risks into scalable product capabilities, including detections, posture checks, and risk signals.
- Contribute to both threat detection and posture management content, ensuring broad coverage across identity, access, integrations, and data exposure risks.
- Analyze large-scale SaaS telemetry to identify patterns, anomalies, and opportunities for new detections or improvements.
- Continuously improve detection quality by reducing false positives and ensuring signals are actionable for customers.
- Collaborate with Engineering to productionize detection logic and ensure reliable execution at scale.
- Partner with Product to shape how security insights are surfaced, prioritized, and explained to users.
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