stripe
Security Incident Response Engineer
About this role
Stripe seeks a Security Incident Response Engineer to investigate threats, build detection systems, and analyze security events at scale. You'll combine behavioral analytics and endpoint hardening expertise to differentiate legitimate from malicious activity and lead incident response improvements across the organization.
What you'll do
- Investigate threats and suspicious activities on client devices using log analysis and forensic techniques
- Develop detection models and requirements for enhanced security systems
- Collect and transform raw security data from multiple sources into detection pipelines
- Optimize incident response tooling and processes for efficiency and clarity
- Collaborate with security engineering and data science teams to build scalable threat detection solutions
- Mentor team members and serve as subject-matter expert for security analytics and detection initiatives
What they're looking for
- Python and SQL programming
- Log analysis and digital forensics
- Behavioral security analytics and threat investigation
- Data pipeline development and data transformation
- Network security and incident response
- Big data tools (Spark, Pandas, Jupyter, Trino)
- SIEM, UEBA, or security automation platforms
- Threat intelligence and threat actor analysis
Opens the official application on the employer’s site. No login required.
stripe
Stripe builds payment infrastructure and financial services platforms, offering APIs and tools that enable developers and businesses to process transactions, detect fraud, verify identity, and manage security at scale. The company is hiring Backend Engineers, Full Stack Engineers, ML Engineers, AI Engineers, and Security Engineers to develop core platform systems, payment intelligence, customer support infrastructure, and security data platforms.
- Website
- stripe.com
Likely interview questions
- Walk us through how you've used behavioral analytics to identify a sophisticated threat in a past role.
- How would you approach building detection models when you have incomplete or noisy telemetry data?