Designated Technical Support Engineer, Ops
$120k - $175k • Remote • Mountain View, CA
Posted 11h ago
About the job
Glean is seeking a highly analytical individual to join the Support Organization and focus on enhancing the effectiveness of our Support team through metrics, AI-powered tooling, and operational excellence. This role involves defining, tracking, and refining key support metrics, building dashboards and reports for leadership visibility, and presenting insights and recommendations. You will also be the operational owner for AI-powered support tools, analyzing their performance and running experiments to improve outcomes. Additionally, you will partner with Product, Engineering, and Data Science to scope and launch new AI use cases for Support.
Responsibilities
- Define, track, and refine support metrics such as ticket volume, time to resolution, CSAT, deflection, automation rate, containment, backlog health, and agent effort.
- Build and maintain dashboards and reports in BI tools (e.g., Sigma, Looker, Tableau) for performance and trend visibility.
- Present insights and recommendations to leadership, highlighting risks, opportunities, and the impact of AI/automation initiatives.
- Serve as the operational owner for AI-powered support tools, including configuration, monitoring, and continuous improvement.
- Analyze agent and automation performance and run experiments on prompts, workflows, and routing rules to improve outcomes.
- Partner with Product, Engineering, and Data Science to scope and launch new AI use cases for Support.
- Ensure AI/automation features are rolled out with a focus on enterprise trust and scalability.
Requirements
- Highly analytical individual
- Experience with metrics, reporting, and insights
- Experience with AI-powered tooling, agents, and automation
- Experience with BI tools (e.g., Sigma, Looker, Tableau)
- Ability to analyze performance and run structured experiments
- Ability to partner with Product, Engineering, and Data Science teams
- Understanding of enterprise trust and scalability considerations for AI features