Stripe

Stripe

Stripe is a global technology company that provides financial infrastructure for the internet. They offer a suite of APIs and tools for businesses to accept online and in-person payments, automate financial processes, and embed financial services in th...

Diversified Financial Services
5K-10K
Founded 2009
$8700M raised

Description

  • Design, build, and tune high-fidelity detections across modern SIEM platforms for adversary techniques across the full attack lifecycle.
  • Develop detection hypotheses by researching attacker tradecraft, identifying evidence sources, and mapping detection opportunities to available telemetry.
  • Conduct hypothesis-driven threat hunts to uncover malicious activity, identify detection gaps, and validate security controls.
  • Perform malware analysis and reverse engineering to extract indicators and inform detection strategies.
  • Build network-based and endpoint-based detections across Windows, Linux, and macOS using logs, EDR telemetry, memory artifacts, and protocol analysis.
  • Partner with Threat Intelligence to turn intel reports into detections, hunting leads, and enrichment logic.
  • Collaborate with incident response, SOC, and offensive security teams to validate and refine detections using real-world incidents and red team exercises.
  • Build data pipelines, automation, and tooling to support detection-as-code and scalable deployment.
  • Map and prioritize detection coverage against MITRE ATT&CK across key attack surfaces.
  • Lead projects, mentor teammates, and uphold quality standards within the team.

Requirements

  • 5+ years of experience in detection engineering, threat hunting, or security operations.
  • Experience writing detection logic in modern SIEM platforms such as Splunk, Chronicle, Elastic, CrowdStrike NG-SIEM, Panther, or Microsoft Sentinel.
  • Strong understanding of adversary tradecraft across the attack lifecycle, including initial access, privilege escalation, lateral movement, defense evasion, persistence, and exfiltration.
  • Ability to extract TTPs from threat intelligence reports and translate them into detection opportunities.
  • Experience developing network-based and endpoint-based detections across Windows, Linux, and macOS.
  • Experience analyzing telemetry from endpoint, network, cloud environments such as AWS, GCP, or Azure, identity, and application log sources.
  • Proficiency in detection/query languages such as SPL, KQL, EQL, YARA-L, or SQL, and programming in Python or a similar language.
  • Strong communication skills with the ability to document detection logic and explain findings to technical and non-technical audiences.
  • Adversarial mindset with the ability to think like an attacker to build effective detections.
  • Experience in fintech, financial services, or highly regulated environments is preferred.
  • Background in malware analysis, reverse engineering, or threat research is preferred.
  • Experience with purple team operations and collaborative detection validation is preferred.
  • Familiarity with big data platforms such as Databricks, Trino, or PySpark for large-scale log analysis is preferred.
  • Proficiency with AI/LLM-assisted development tools such as Claude Code, Cursor, or GitHub Copilot applied to detection workflows is preferred.
  • Interest in agentic automation using LLMs to augment hunting, tuning, or triage is preferred.
  • Experience with detection validation tools such as Atomic Red Team or ATT&CK Evaluations is preferred.
  • Contributions to open-source detection content, research, or conference presentations are preferred.
  • Relevant certifications such as HTB CDSA, GCIH, GCFA, GNFA, OSCP, TCM PMAT, or GREM are preferred.

Interested in this position?

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