Detection Engineering Lead – Taiwan

1 нед. назад

TaiwanWorldwideLead

aioauthsecurity

Lead the detection engineering efforts to secure SaaS applications by detecting anomalous activities and managing integration risks.

Обязанности

Obsidian Security is looking for a Detection Engineering Lead to establish and lead detection engineering capabilities within our growing Taiwan organization.
You will lead the development of security detections that identify identity threats, malicious activity, risky behavior, and emerging attack patterns across cloud and SaaS environments. The role combines threat research, detection modeling, data analysis, rule authoring, and production engineering.
You will work closely with Security Research, Product Management, platform engineering, data engineering, and Customer Success to translate real-world threats into accurate, explainable, and operationally effective detections.
This is a hands-on leadership role. You will shape detection strategy and processes while directly contributing to threat models, detection logic, investigations, and validation. You will also build and mentor a Taiwan-based team as the organization grows.

Будет плюсом

Experience with identity security, SaaS security, cloud detection and response, SIEM, UEBA, EDR, or XDR products.
Deep familiarity with identity-based attacks, including account takeover, session abuse, privilege escalation, persistence, and lateral movement.
Experience with MITRE ATT&CK or similar threat-modeling and coverage frameworks.
Experience building detection-as-code systems, rule languages, detection engines, or automated validation frameworks.
Experience applying machine learning, anomaly detection, or generative AI to security problems.
Experience investigating real-world intrusions or working directly with incident response teams.
Experience building or scaling a detection engineering team.
Experience working in Taiwan or with globally distributed APAC teams.
Mandarin proficiency.

Другое

Obsidian Security is the leading SaaS security platform, trusted by global enterprises like Snowflake, T-Mobile, and Algolia. We protect 200+ organizations across North America, Europe, the Middle East, Southeast Asia, Australia, and New Zealand, including many of the world’s largest Fortune 1000 and Global 2000 companies.
Founded in 2017 and backed by top investors like Greylock, Obsidian was built to close a critical gap: securing SaaS apps where business happens—Microsoft 365, Salesforce, and hundreds more. The company does this by offering a complete SaaS security platform to reduce risk, detect and respond to threats, and prevent breaches at the source. Obsidian was built by leaders who redefined endpoint and identity security at CrowdStrike, Okta, Cylance, and Carbon Black. Now, they’re transforming how SaaS is secured.
With AI driving rapid SaaS growth and complexity, agentic AI tools gain privileged access to sensitive data through integrations, creating new risks most security tools miss. Obsidian uniquely detects anomalous OAuth token activity and manages integration risks. Major announcements are on the horizon. Recognizing that SaaS security needs to evolve, Obsidian enables growing organizations to start with a lightweight, prevention-focused browser extension and expand coverage over time.
With global momentum, a growing partner ecosystem including SentinelOne, Databricks, and Google Cloud, and a major fundraise ahead, Obsidian is scaling rapidly toward long-term growth and IPO readiness.
Establish and lead the detection engineering function in Taiwan, including its technical direction, operating model, quality standards, and hiring plan.
Research cloud, identity, and SaaS threats and translate attacker behaviors into actionable detection opportunities.
Develop threat models covering attack paths, adversary techniques, identity misuse, suspicious activity, and control failures.
Design, author, test, and maintain detection rules and behavioral detection models.
Define the telemetry, enrichment, correlation, and historical context required to support effective detections.
Partner with platform and data engineering teams to build scalable detection capabilities and production processing pipelines.
Develop frameworks for detection testing, simulation, coverage measurement, versioning, release management, and ongoing tuning.
Measure and improve detection precision, recall, explainability, performance, and customer value.
Investigate false positives and false negatives, identify their root causes, and improve detection logic and underlying data quality.
Map detection coverage to relevant threat frameworks, attack techniques, product use cases, and customer risks.
Monitor the evolving threat landscape and rapidly develop coverage for new techniques and vulnerabilities.
Partner with Product Management and Customer Success to understand customer environments, workflows, and detection requirements.
Create clear documentation explaining detection intent, supporting evidence, expected behavior, limitations, and recommended response actions.
Mentor detection engineers and security researchers while establishing a culture of scientific rigor, curiosity, and continuous improvement.
Collaborate with teams across Taiwan, the US, the UK, and Australia to build a unified global detection program.
Significant experience in detection engineering, threat research, security analytics, incident response, threat hunting, or a closely related security discipline.
Experience leading security research or detection initiatives and mentoring other practitioners.
Strong knowledge of attacker behavior, identity threats, cloud security, and modern enterprise SaaS environments.
Demonstrated experience developing production detections using rules, queries, correlations, statistical methods, or behavioral models.
Experience working with large security datasets, including audit events, authentication activity, identity data, application logs, or cloud telemetry.
Ability to distinguish malicious behavior from legitimate activity and translate that reasoning into precise, maintainable detection logic.
Strong data analysis skills and proficiency with relevant query, scripting, or programming languages.
Understanding of detection lifecycle management, including research, development, validation, deployment, monitoring, tuning, and retirement.
Experience measuring detection quality and using production feedback to improve security outcomes.
Ability to communicate technical security concepts clearly to engineering, product, customer-facing, and executive audiences.
Strong written and verbal communication skills in English.
Ability to collaborate effectively across regions, time zones, functions, and cultures.
Obsidian has a disciplined and scalable detection engineering capability in Taiwan.
New threats are converted into production-quality detections quickly and reliably.
Detection coverage, quality, and customer impact can be measured and continuously improved.
False positives are controlled without sacrificing meaningful threat coverage.
Detection engineers, researchers, and platform teams work through a clear and effective development lifecycle.
Customers receive timely, explainable, and actionable security findings.