Threat Detection Engineer

About the Role: Grade Level (for internal use): 11The Team The Cyber Fusion Center protects the business by combining threat intelligence, monitoring, detection engineering, incident response, and security automation into one proactive, intelligence-led defense capability. By breaking down silos, the team reduces risk and strengthens security across the enterprise. The team prioritizes collaboration, continuous learning, and innovation, with shared ownership of outcomes and a supportive learning mindset during high-pressure situations. This is a hands-on, build-driven environment where team members work with real-world use cases, rapidly develop new capabilities, and continuously improve the organization’s ability to detect, investigate, and respond to emerging threats. Responsibilities and Impact Design, develop, test, and maintain high-quality detections for threats including malware, ransomware, identity attacks, insider threats, cloud threats, network, AI, and advanced persistent threats. Develop detection logic across SIEM, EDR/XDR, cloud, network, identity, and application security platforms using tools such as Splunk SPL, Microsoft Sentinel KQL, Elastic Query DSL, Sigma, and YARA. Apply artificial intelligence, machine learning, and data science techniques to identify anomalous behavior, emerging threats, attack patterns, and indicators of compromise. Build and maintain security analytics and data pipelines using Python, SQL, Pandas, NumPy, scikit-learn, and Databricks. Perform feature engineering, statistical analysis, model development, testing, tuning, and performance monitoring for security use cases. Conduct threat hunting and hypothesis-driven investigations to identify gaps in existing detection coverage. Map detections to the MITRE ATT&CK framework and improve coverage of priority attack techniques. Analyze false positives, false negatives, alert quality, and detection performance, implementing improvements based on measurable results. Collaborate with SOC, Incident Response, Threat Intelligence, SOAR, Cloud Security, and Vulnerability Management teams to develop effective detection capabilities. Implement detection-as-code practices using version control, peer review, automated testing, CI/CD, documentation, and controlled deployment. Support purple-team exercises, adversary simulations, attack replay, and detection validation activities. Explore the use of Generative AI, Large Language Models, and Agentic AI to improve threat research, detection development, alert analysis, and investigations. Develop Power BI dashboards and reports to communicate detection coverage, alert trends, model performance, data quality, and operational metrics. Document detection logic, data sources, analytical methods, model limitations, investigation procedures, and response recommendations. What We’re Looking For