Incident Systems Analyst

TEKsystems•Kirkland, WA

6d•$45 - $60•Remote

About The Position

The analyst will focus on incident analysis and telemetry validation, helping ensure that this client’s detection systems are accurate, actionable, and continuously improving. This includes analyzing high-priority incidents (Sub-0 to Sub-2), understanding system behavior through telemetry signals, and identifying opportunities to improve detection logic and reduce noise in alerting systems. The work directly impacts incident detection accuracy, response speed, and overall service reliability. This is not a traditional reporting or dashboarding role. Instead, it is a hands-on investigative position requiring strong analytical thinking, technical curiosity, and the ability to interpret complex system behavior. You will spend your time investigating incidents, validating signals, and presenting findings, often working with ambiguous or incomplete data. While tools like KQL are important, the role increasingly emphasizes understanding the data and systems, rather than just generating queries, especially as AI augments query development. Two analysts will share this workload, splitting responsibilities across ongoing incident analysis.

Requirements

3 YOE data analysis (SQL, PowerBI or Grafana, excel) and incident investigation experience
Working proficiency (3 YOE) in KQL or similar query tools
2 YOE in electrical or mechanical systems monitoring (mechanical preferred)

Nice To Haves

Experience with Python scripting is beneficial, particularly for data interpretation and lightweight analysis.
Familiarity with data center operations, SRE environments, or large-scale monitoring systems is also valuable.

Responsibilities

Review overnight incidents and telemetry signals, identifying anomalies or trends that require deeper investigation.
Perform structured analysis on high-priority incidents, examining data patterns, validating signal accuracy, and determining root causes.
Participate in daily syncs where you present your findings, explain system behavior, and collaborate with engineers and SMEs to refine detection logic.
Ask the right questions—understanding why an issue occurred, when it started, and what signals support the conclusion.
Maintain strong data hygiene practices, including accurate tagging and classification of incidents.
Contribute to improving telemetry pipelines by identifying gaps or inconsistencies.
Use tools like KQL to explore datasets, while validating outputs carefully to ensure accuracy and avoid erroneous conclusions.