Technical Operations Analyst II

Expedia Group•Austin, TX

4d•$87,000 - $121,500

About The Position

The Technical Operations Analyst II will help keep Expedia Group’s enterprise AI platforms reliable and turn platform data into useful decisions. The role sits between engineers responsible for telemetry and integrations, vendors managing parts of the technology stack, and leaders who depend on those services. It involves investigating incidents, monitoring platform signals, and reporting on AI tool performance, adoption, usage, and costs. This is a technical, data-driven analyst position, not a software engineering role. The analyst will own observability and reporting for AI platforms and integrations; coordinate vendor issues and access to vendor-held telemetry, usage, and audit data; lead incident response and cross-team investigations; and improve monitoring, runbooks, and preventive checks. The role also builds BI dashboards, analyzes demand and capacity, identifies automation opportunities, creates self-service knowledge content, supports cross-team rollouts and adoption, and mentors peers while building relationships across technical and business teams. The AI & Automation Team onboards and manages AI tools and partners across Expedia Group to create products and services. It provides employees worldwide with high-quality technology experiences to boost productivity and enable AI in the flow of work.

Requirements

Bachelor's degree, master's degree, or equivalent professional experience.
At least 2 years of experience in technical operations, application or production support, or monitoring; alternatively, 0–2 years of experience with a master's degree.
Hands-on incident and problem management experience, including coordinating response across teams and writing root-cause and incident summaries.
Experience with monitoring and observability tools such as Datadog, Splunk, Dynatrace, or CloudWatch, including building dashboards and tuning alerts.
Experience managing vendor or third-party support relationships, including tracking issues to resolution against service levels.
Ability to analyze data for trends and report findings using spreadsheets and a BI or dashboard tool.
Working knowledge of AI assistants and LLM-based tools, including validating AI-generated guidance and identifying hallucinations or risky outputs.
Clear written and verbal communication skills, including explaining technical findings to technical and non-technical audiences.
Ability to quantify the impact of operational issues and propose cost-effective fixes.

Nice To Haves

Experience with OpenTelemetry or similar telemetry pipelines, and SLO and SLI reporting.
Experience managing SaaS or enterprise platform vendors, including escalations and service-level reviews.
Experience administering or supporting enterprise AI or knowledge platforms such as Glean, Claude, or similar, including usage, adoption, and cost reporting.
Familiarity with FinOps or cost-attribution practices for cloud or AI spend.
Experience with an ITSM platform such as ServiceNow and ITIL Foundation knowledge.
Experience with Looker, Tableau, or a similar dashboard tool, and defining metrics against a shared metrics platform or data lake.
Experience writing knowledge articles and runbooks designed for self-service and AI search.
Experience leading change or tool adoption across teams.

Responsibilities

Own observability for AI platforms and integrations, turning OpenTelemetry and platform telemetry into reporting and recommendations on reliability, performance, usage, and cost, including token consumption and spend.
Manage vendors for vendor-managed applications by tracking issues to resolution against agreed service levels, escalating when needed, and obtaining telemetry, usage, and audit data.
Lead first response and cross-team incident investigations, restore service quickly, write evidence-based incident reports, and improve runbooks and preventive checks.
Design SLO-aligned monitors, tune alerts to reduce noise, and correlate tickets, alerts, telemetry, and vendor reports to identify systemic risks and prioritize fixes by impact.
Build and maintain BI dashboards and reports with metric definitions consistent with enterprise standards, and model demand and capacity to recommend changes.
Identify routine diagnostic and reporting tasks for automation and partner with engineering owners to implement automation.
Design AI-ready knowledge articles, runbooks, and self-service content, and measure ticket deflection and customer satisfaction.
Plan and run cross-team changes, including tool rollouts and feature adoption, and measure uptake.
Mentor peers and build relationships across engineering, support, security, vendors, and business teams.