Senior Network Security Analyst II - SIEM & IR Expert

HonorVet Technologies; Austin (TX); On-site; USD 90,000 - 130,000; Part time

Job summary

HonorVet Technologies is seeking an experienced Network Security Analyst II to support enterprise cybersecurity operations from Austin, TX. The role is onsite and contracted for 11 months with potential extension, focusing on monitoring, investigation, and response to security events.

The candidate will use Microsoft Sentinel, SIEM, EDR, and NDR tools, develop detection rules, and perform threat hunting. Strong knowledge of KQL and SPL is highly preferred, along with a solid background in

Qualifications

• 7+ years in cybersecurity, network security, or incident response.
• Experience with SIEM/SOAR/EDR/NDR technologies.
• Hands-on with Microsoft Sentinel and writing security queries.
• Knowledge of threat intel and indicators of compromise.
• Familiarity with NIST/CIS/HIPAA controls.

Responsibilities

• Monitor security alerts, network traffic, logs and threat intel feeds.
• Investigate suspicious activity, vulnerabilities, and incidents.
• Perform triage, escalation, and documentation.
• Create and maintain detection rules, dashboards, alerts, and playbooks.
• Conduct threat hunting using KQL, SPL, and endpoint telemetry.
• Collaborate with network, cloud, and app teams to resolve issues.
• Prepare incident reports and metrics for audits and reviews.

Skills

Threat detection

Incident response

Analytical thinking

Team collaboration

Education

Bachelor's degree in Cybersecurity

Tools

Microsoft Sentinel

KQL

SPL

EDR/NDR

Job description

HonorVet Technologies is seeking an experienced Network Security Analyst II to support enterprise cybersecurity operations from Austin, TX. The role is onsite and contracted for 11 months with potential extension, focusing on monitoring, investigation, and response to security events.

The candidate will use Microsoft Sentinel, SIEM, EDR, and NDR tools, develop detection rules, and perform threat hunting. Strong knowledge of KQL and SPL is highly preferred, along with a solid background in