Principal Software Development Engineer - Developer Productivity & Insights

Expedia Expedia · Hospitality · IL

Principal Software Development Engineer role focused on Developer Productivity & Insights within Expedia Group's Technology Team. This role involves defining strategy, metrics, and data models for engineering effectiveness, leading analytical investigations, partnering with teams to experiment with changes, and shaping AI-era productivity strategy. The goal is to improve developer satisfaction and productivity across the organization by leveraging data and insights.

What you'd actually do

  1. Define and evolve the end-to-end strategy, metrics framework, and data model for Developer Productivity & Insights, ensuring it aligns with industry best practices
  2. Design and maintain robust data pipelines, schemas, and dashboards that integrate SDLC signals (Git, CI/CD, Jira, incident/change data, AI tooling) into a coherent, trusted view of engineering effectiveness.
  3. Lead deep analytical investigations into developer productivity, flow, and quality, turning noisy telemetry into clear narratives, causal hypotheses, and concrete recommendations for senior engineering leadership.
  4. Partner with DevEx, Platform, and product teams to experiment with changes to tools, workflows, and guardrails, measuring impact (before/after) and operationalizing practices that demonstrably improve outcomes.
  5. Define, validate, and iterate productivity “north star” and leading indicators (e.g., repo readiness, power-user profiles, AI usage depth) and prune or simplify metrics that don’t drive decisions.

Skills

Required

  • Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
  • 10+ years of software development experience delivering and operating large-scale, distributed systems or platforms, including ownership of multiple services or a significant technical domain.
  • Experience leading developer relations or technical evangelism efforts at scale in enterprise or platform environments
  • Proven expertise in designing and implementing service architectures, including system design (LLD), API design, and data modeling, with strong proficiency in at least one modern programming language and associated ecosystems.
  • Demonstrated experience driving engineering best practices (testing, observability, performance, reliability, security) and leading complex initiatives from design through production operation.
  • Familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products, with the ability to safely integrate and operate AI/ML‑enabled components within existing services.

Nice to have

  • Experience with developer productivity, platform engineering, or horizontal core services teams, including ownership of CI/CD tooling and deploying code at scale.
  • Deep, hands-on understanding of the software development lifecycle (SDLC), CI/CD pipelines, and getting code safely to production in large, distributed systems.
  • Strong data science and analytics background; skilled at combining multiple data sources, applying analytical techniques, and turning findings into actionable insights for engineering leadership.
  • Hands-on experience measuring developer productivity using frameworks such as DORA or SPACE
  • Demonstrated executive presence with a track record of translating complex technical and data concepts into clear narratives and data stories for senior engineering and CTO-level leaders.
  • Practical experience with AI developer tools (e.g., Claude, GitHub Copilot, Cursor, Codex, Kiro), including evaluating ado

What the JD emphasized

  • defining how we measure and understand engineering effectiveness
  • evaluating how AI is changing developer workflows
  • shape AI-era productivity strategy
  • measuring developer productivity