Technical Program Manager II (AI and Analytics)

Expedia•Seattle, WA

4d•$119,000 - $190,500•Onsite

About The Position

The Technical Program Manager II independently drives execution of cross-functional technical programs from planning through launch and post-launch measurement, helping teams deliver complex work with greater clarity, speed, and consistency. This role partners closely with engineering, product, and business stakeholders to manage scope, schedules, dependencies, risks, communications, and operating rhythms across multiple workstreams. They bring structure to ambiguity, create scalable program mechanisms, use data and metrics to track progress and inform decisions, and translate technical concepts, trade-offs, and delivery risks for both technical and non-technical audiences. As a force multiplier for the team, they improve coordination, strengthen execution, and enable technical partners to stay focused on delivery. The AI and Analytics team builds scalable capabilities that help Expedia Group teams access trusted data, generate insights faster, and apply AI in practical, measurable ways. The team works at the intersection of analytics, data platforms, and AI-enabled products to improve self-service decision-making, standardize metrics and datasets, enhance dashboards and scorecards, and create reusable tools and operating mechanisms that support both technical and business stakeholders. The group partners closely with product, engineering, and analytics teams to drive adoption of modern AI and analytics solutions while maintaining a strong focus on quality, usability, governance, and business impact.

Requirements

4+ years of experience in Technical Program Management, Engineering Program Management, Product Operations, or a related role in a technology environment.
Proven experience managing cross-functional technical programs from planning through execution, launch, and post-launch measurement.
Experience partnering closely with engineering and product teams to manage program scope, schedules, risks, dependencies, and stakeholder communications.
Strong functional and high-level technical understanding of software platforms, with the ability to explain product capabilities, technical concepts, trade-offs, and delivery risks to varied audiences.
Exceptional written and verbal communication skills, including the ability to produce clear summaries, executive updates, program reviews, meeting notes, and decision documents.
Highly organized and detail-oriented, with a track record of creating repeatable operating mechanisms such as dashboards, scorecards, risk registers, launch checklists, and status reporting frameworks.
Data-driven approach to program management, with ability to use metrics and analytics to track progress, identify risks, summarize team output, and support decision-making.
Ability to bring structure to ambiguity, drive alignment across stakeholders, and ensure clear ownership and follow-through across multiple workstreams.

Nice To Haves

Experience working on a platform team, preferably in AI/ML, GenAI, developer platforms, data platforms, cloud infrastructure, APIs, internal tooling, or enterprise software.
Familiarity with GenAI concepts such as LLMs, prompt engineering, RAG, agents, model evaluation, inference workflows, AI safety, or AI product adoption.
Experience creating analytics-rich program artifacts such as adoption dashboards, usage reports, team output summaries, quarterly business reviews, roadmap health reports, or leadership scorecards.
Demonstrated ability to influence without authority and drive accountability across teams with competing priorities.

Responsibilities

Independently drives execution of cross-functional technical programs from planning through launch and post-launch measurement.
Partners closely with engineering, product, and business stakeholders to manage scope, schedules, dependencies, risks, communications, and operating rhythms across multiple workstreams.
Brings structure to ambiguity, creates scalable program mechanisms, uses data and metrics to track progress and inform decisions.
Translates technical concepts, trade-offs, and delivery risks for both technical and non-technical audiences.
Improves coordination, strengthens execution, and enables technical partners to stay focused on delivery.
Drives adoption of modern AI and analytics solutions while maintaining a strong focus on quality, usability, governance, and business impact.