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We are proud to have the best of both worlds — a startup culture with the resources of a large global public company. This fuels us to continue our growth and launch new services at the speed we have been since our inception. We are all entrepreneurs surrounded by opportunities to drive new initiatives and innovations. At our core, we are bold and ambitious people that like to get our hands dirty and make a hands-on impact. At Coupang, you will see yourself, your colleagues, your team, and the company grow every day.

Our mission to build the future of commerce is real. We push the boundaries of what’s possible to solve problems and break traditional tradeoffs. Join Coupang now to create an epic experience in this always-on, high-tech, and hyper-connected world.

Role Overview

As a Senior Insider Threat Analyst, you will play a pivotal role in safeguarding our intellectual property, financial assets, employee data, and customer data from internal risks. Reporting directly to the Director of Global Insider Threat, you will lead complex insider threat related analysis, help develop advanced detection use cases, and mature our User and Entity Behavior Analytics (UEBA) capabilities. You will serve as a subject matter expert in Data Loss Prevention (DLP) and insider fraud, acting as a critical bridge between Information Security, Legal, HR, and Compliance teams.

Primary Responsibility

• Advanced Alert Triage & Analysis:

Monitor, analyze, and enrich alerts from various security tools to identify anomalous user behavior indicative of data exfiltration, access abuse, or other insider misconduct.

• Escalation & Case Packaging:

Perform deep-dive behavioral analysis to determine the severity, context, and potential intent behind anomalous activity. Compile high- fidelity data packages and escalate actionable cases to the formal investigations, Legal, or HR teams for final disposition.

• DLP Program Optimization:

Act as a primary analytic stakeholder for our Data Loss Prevention (DLP) ecosystem. Provide feedback to engineering teams to tune policies, reduce false positives, and ensure robust coverage across the environment.

• Insider Fraud Detection:

Analyze access logs and system activities to identify anomalies that may indicate occupational fraud, financial irregularities, or privilege abuse.

• Cross-Functional Handoff:

Partner closely with Information Security, Employee Relations, and Legal teams to ensure a seamless, well-documented handover of triaged cases, ensuring all analysis is conducted ethically and complies with local regulations.

• Program Maturity & Automation:

Proactively identify gaps in current detection and triage capabilities. Champion the use of AI, Machine Learning, and automation to streamline repetitive triage tasks and accelerate the team's time-to-decision.

• Mentorship:

Provide technical guidance to junior analysts on the team, helping to build local capability in the growing field of insider risk analysis

Required Qualifications

• Experience:

4+ years of overall experience in Cybersecurity, Security Operations (SOC), Incident Response, IT Audit, or Data Protection.

• Insider Risk / DLP Exposure:

At least 1-2 years of experience specifically analyzing alerts related to Data Loss Prevention (DLP), User Behavior Analytics (UEBA), or insider threat events. (Note: Candidates with strong general security analytics backgrounds demonstrating a clear aptitude for behavioral analysis will be strongly considered).

• Technical Acumen:

Strong understanding of network architecture, operating systems (Windows, macOS), identity management, and cloud platforms, with the ability to quickly pivot through logs to build a timeline of user activity.

• Analytical Mindset:

Exceptional critical thinking skills with the ability to look beyond a single alert to understand the broader context of user behavior.

• Discretion:

High degree of integrity and the ability to handle highly sensitive, confidential employee data with strict discretion.

Preferred Qualifications

• AI & Automation Skills:

Experience leveraging Artificial Intelligence (AI) and Machine Learning models for anomaly detection, or proficiency in scripting (e.g., Python, PowerShell) to automate analytic workflows.

• Fraud Analysis Experience:

Prior exposure to analyzing technical indicators of corporate fraud, unauthorized data access, or corporate espionage.

• Local Privacy Knowledge:

Familiarity with South Korea's Personal Information Protection Act (PIPA) and its implications for employee monitoring and data security analysis.

• Education:

Bachelor’s degree in Cybersecurity, Computer Science, or a related technical field (or equivalent practical experience).