Title: Network Detection Engineer ( NDR ) Duration: Full Time/Permanent /C2C Location: Minneapolis MN (Remote 100%) Network Detection Specialist Engineer We are seeking a skilled engineer to support our Network Detection & Response (NDR) operations and build a modern health monitoring system for our Corelight security platform. This role is ideal for someone who blends security operations experience with strong automation and monitoring skills.

Key Responsibilities

Support day to day operations of the NDR platform, including sensor health, data flow monitoring, and alert handling. Build and maintain a platform health monitoring solution using Grafana, Prometheus, InfluxDB, and API integrations. Develop automation scripts in Python to collect system and application health metrics. Create dashboards and alerts that provide real time visibility into system performance and uptime. Work with Security Operations, Incident Response, and Network teams to ensure timely detection and resolution of issues.

Required Qualifications

Experience with network security tools, ideally Corelight, Endace, Zeek, or Suricata. Strong Python skills, especially for automation and working with APIs. Hands on experience with monitoring and visualization tools such as Grafana, Prometheus, and/or InfluxDB. Solid understanding of general network concepts (e.g., TCP/IP, traffic flow, packet capture). Background in SOC, NDR monitoring, or security operations is highly desirable. Experience building dashboards, alerts, or custom data collectors. Familiarity with containerized environments (Docker, Kubernetes). Knowledge of API authentication and data integration best practices. Required AI Skills: - All resources are expected to demonstrate baseline proficiency in enterprise-approved AI tools as part of their day-to-day responsibilities. This includes, but is not limited to: -Consistent Use: Maintain a minimum of 90% weekly usage of AI tools such as GitHub Copilot, Microsoft 365 Copilot, and other GenAI platforms approved by the enterprise. -Applied Productivity: Leverage AI tools to enhance coding, documentation, data analysis, and decision-making workflows. -Continuous Learning: Stay current with evolving AI capabilities and features, and apply them to improve delivery