Software Development Engineer III

Expedia•Seattle, WA

19d•Hybrid

About The Position

Expedia Group brands power global travel for everyone, everywhere. We design cutting-edge tech to make travel smoother and more memorable, and we create groundbreaking solutions for our partners. Our diverse, vibrant, and welcoming community is essential in driving our success. The Release Engineering team owns EG's continuous delivery platform - Spinnaker, GitHub Actions, Deployment Orchestrators & Controllers. They are used to ship services from lab to production across multiple cloud and container platforms (for example Kubernetes, ECS and EC2). We define and evolve deployment standards, release policies, and rollout patterns (canary, blue/green, gated production releases, automated judgements, automated rollbacks) so every team can ship quickly without trading off safety. Our platform drives high‑volume, multi‑region releases across multiple products, domains, and environments. We’re increasingly leveraging AI and ML across the delivery ecosystem to improve observability, speed up troubleshooting, and make releases safer and the experience more delightful for the engineers.

Requirements

Bachelor’s degree in Computer Science or a related technical field; or Equivalent related professional experience.
5+ years of relevant professional experience.
Professional experience building and operating backend or full-stack services, including responsibility for at least one significant service or feature in production.
Proficiency in at least one modern programming language and associated frameworks, with hands-on experience in RESTful API design, data modeling, and system design (low-level design) for distributed services.
Experience owning the full lifecycle of a service (design, development, testing, deployment, and operations), including working with CI/CD pipelines, source control, and cloud-based environments.

Nice To Haves

7–8+ years of experience building and operating backend or platform services in large-scale, distributed environments, ideally in developer experience, release engineering, or platform engineering teams.
Deep, hands-on experience with AWS, Kubernetes, Terraform, and CI/CD pipelines, including designing and evolving internal tooling used by multiple engineering teams.
Proven track record improving release safety and reliability (e.g., reducing deployment-related incidents, improving rollout speed/latency, increasing success rate of releases) using metrics, observability tooling, and data-driven decision making.
Experience designing and iterating on service and API contracts, schemas, and deployment workflows to support safe, consistent, and auditable changes to production.
Demonstrated ability to lead technical delivery for complex backend/platform or DevOps initiatives, influencing design decisions, and mentoring others in topics such as system design, testing strategies, CI/CD best practices, and secure, scalable coding.
Demonstrated AI fluency: using AI tools effectively in design and coding workflows, understanding their limitations, and (bonus) experience integrating or operating AI-assisted capabilities within engineering tooling or platforms.

Responsibilities

Own, design, and implement medium-to-large scale services and features, including system design (low-level design), API design, and data modeling to support highly available, performant travel platforms.
Write clean, testable, and maintainable code across the full software development lifecycle, including unit, integration, and contract tests, ensuring high quality and reliability in production.
Collaborate with product, design, and cross-functional engineering teams to decompose complex business problems, define clear technical solutions, and deliver customer-centric outcomes across multiple domains.
Drive operational excellence for your services by defining SLIs/SLOs, improving observability, and implementing robust monitoring, alerting, and incident response practices.
Safely integrate and operate AI/ML‑enabled solutions that improve outcomes, including familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products.
Contribute to shared engineering standards, reusable components, and best practices, mentoring other engineers and improving team effectiveness across different problem spaces and technologies.