Detection Engineer, Manager

Capital One•McLean, VA

2d•$197,300 - $225,100

About The Position

Capital One's Cyber Organization is a fast-paced, dynamic environment committed to enabling and securing the business. Our Cyber Operations & Intelligence division is searching for an experienced Individual Contributor (IC), Manager-level Detection Engineer with deep endpoint security expertise to join our Cyber Threat Detection (CTD) team. In this role, you will own end-to-end detection coverage for our Endpoint domain — from telemetry requirements and threat landscape awareness through to coverage gap identification and high-fidelity alert deployment. The CTD team engineers, deploys, and validates automated threat detections that protect Capital One's customers, assets, and associates. Our detections power the investigations and incident response work of the Cyber Security Operations Center (CSOC), running continuously across our enterprise SIEM, which processes over 20 million events per second across hundreds of custom detection rules and thousands of out-of-the-box. Underpinning this platform is a Detection-as-Code discipline: every detection is a YAML-based rule, version-controlled in GitHub, CI/CD deployed, peer-reviewed, and unit-tested. In this role, you will be a senior technical contributor responsible for solving hard problems using cutting-edge technology across endpoint security engineering, alert development, EDR/endpoint log analysis, and monitoring at scale.

Requirements

Deep expertise in endpoint security, EDR platforms, and Windows, Linux, macOS telemetry analysis
Previous experience on a detection engineering, threat detection, or detection operations team with a focus on endpoint threat surfaces
Extensive experience with SQL and data querying at scale
Strong understanding of attacker TTPs, Red Team methodologies, and translating offensive security insights into high-fidelity detections
Experience with ML or data science concepts applied to security use cases (anomaly detection, behavioral analytics, risk scoring)
Excellent analytical, communication, and cross-functional collaboration skills
Ability to perform independent root cause analysis and convey complex security risks to both technical and executive audiences
Demonstrated ability to mentor engineers and contribute to a culture of continuous improvement
High School Diploma, GED, or equivalent certification
At least 4 years of experience working in cybersecurity or information technology
At least 4 years of experience with endpoint and host logs (Windows Event Logs, Sysmon, EDR telemetry)
At least 2 years of experience with EDR platforms (CrowdStrike Falcon, SentinelOne, Microsoft Defender)
At least 4 years of experience developing alerts for threat detection
At least 2 years of experience with penetration testing, offensive security, or adversary emulation
At least 1 year of experience with machine learning or data science applied to security

Nice To Haves

Bachelor's Degree
6+ years of experience in Threat Detection, Threat Hunting, or Security Engineering
4+ years of experience with Python
4+ years of experience with data science concepts and techniques (anomaly detection or behavioral analytics)
2+ years of experience publishing code to GitHub using CI/CD workflows
Experience with Databricks, Apache Spark, or streaming data platforms
Experience with Detection-as-Code methodologies and YAML-based detection frameworks
Experience with CrowdStrike Falcon (custom IOAs, detection tuning, telemetry analysis)
Experience with Windows internals, process injection techniques, LOLBAS, and fileless attack detection
Experience with endpoint forensics, malware triage, or adversary emulation exercises targeting endpoint environments
2 or more professional certifications (GCIA, GCIH, CISSP, GMON, GREM, GCTD, MLE, or AWS Cloud Practitioner, AWS Security)

Responsibilities

AI-Driven Detection & Engineering: Leverage LLMs and machine learning to automate detection logic, summarize complex attack chains, reduce false positives, and accelerate the full detection development lifecycle using tools such as Capital One's Detection Engineering Assistant.
Detection-as-Code (DaC) Leadership: Lead the design, development, and maintenance of detection rules using DaC methodologies, utilizing GenAI-assisted development workflows and CI/CD pipelines against the Cyber Detection Library (CDL).
Behavioral Detection Engineering: Design and build high-fidelity behavioral detections that identify adversary patterns, TTPs, and anomalous endpoint activity — leveraging user and entity behavior signals, process telemetry, and correlation logic to distinguish malicious from benign activity at scale.
Strategic Architecture: Utilize the MITRE ATT&CK framework to visualize, prioritize, and close endpoint coverage gaps; drive detection architecture decisions that balance fidelity, volume, and operational risk across the endpoint threat surface.
Offensive Alignment & Threat Research: Apply a deep understanding of Red Team methodologies and adversary TTPs to conduct hypothesis-driven threat research across enterprise endpoint environments — translating attacker techniques into high-fidelity detections and proactively identifying coverage gaps before they are exploited.
Endpoint Domain Ownership: Own end-to-end detection coverage for the Endpoint threat surface — managing the full lifecycle from telemetry onboarding through alert deployment, tuning, and continuous coverage gap analysis in collaboration with the Coverage Review Team.
Stakeholder & Risk Management: Partner with business leaders, CSOC, Cyber Threat Intelligence, and Cyber Threat Hunt to ensure robust monitoring across endpoint environments while ensuring all documentation meets strict fintech compliance and audit standards.
Technical Leadership & Mentorship: Serve as a technical bar-raiser across the team — mentoring engineers on traditional security concepts, emerging AI-driven workflows, and detection engineering best practices.