Principal Software Development Engineer - Developer Productivity & Insights

Expedia Group•San Jose, CA

8d•$214,500 - $398,500•Hybrid

About The Position

This Principal Software Development Engineer role is part of the Developer Experience team, at the intersection of DevEx, data, and business strategy. The Developer Experience team provides tools, platforms, metrics, and enablement programs that reduce friction for engineers, streamline software delivery workflows, and improve developer satisfaction and productivity across Expedia Group. In this role, you will focus on Developer Productivity & Insights—defining how we measure and understand engineering effectiveness, partnering with platform and product teams to synthesize signals across SDLC systems and translating those insights into clear narratives and recommendations for senior engineering leadership up to the CTO office. Rather than owning a single platform, you will drive change through influence: shaping our metrics strategy, evaluating how AI is changing developer workflows, and turning data into better tools, workflows, and outcomes for engineers across Expedia Group.

Requirements

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 Haves

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 adoption, usage depth, and impact on developer workflows and outcomes, with concrete examples of using AI in day-to-day work.
Experience building analytics and reporting that answer executive questions about AI adoption, productivity impact, and quality/reliability outcomes (e.g., monitoring and interpreting change failure rate and related metrics).
Understanding of causal inference methods to measure the impact of tooling and workflow changes and proficiency in SQL and modern data platform tooling (e.g., Spark, Databricks) for building and maintaining analytical pipelines at scale.

Responsibilities

Define and evolve the end-to-end strategy, metrics framework, and data model for Developer Productivity & Insights, ensuring it aligns with industry best practices
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.
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.
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.
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.
Act as the technical owner for engineering-wide insights tooling (e.g., LinearB/SEI platforms, internal analytics services), setting standards for data quality, instrumentation, and observability.
Collaborate closely with the CTO office and senior engineering leaders to shape AI-era productivity strategy, including how AI-assistive and agentic workflows are measured, governed, and reported.
Mentor engineers and technical leaders on using data to run better teams—from instrumenting services and repos to interpreting metrics, designing experiments, and avoiding misuse of vanity metrics.