airbnbFeb 11, 2026

My Journey to Airbnb — Anna Sulkina (opens in new tab)

Anna Sulkina’s career journey moved from hardware diagnostics and frontend development into backend infrastructure and engineering leadership. Her experiences at Twitter taught her to design distributed systems for failure and to build consensus around transformative technologies like GraphQL. She joined Airbnb in 2022 because it aligned her passion for travel with an opportunity to strengthen developer infrastructure, organizational strategy, and engineering collaboration. ## Discovering Technology in Post-Soviet Ukraine - Sulkina grew up in Eastern Ukraine as the Soviet Union collapsed. - Her older brother introduced her to computers by bringing home hardware components and assembling a machine that loaded programs from a cassette player. - Seeing how individual components formed a working system inspired her to pursue technology. ## Learning English While Building Technical Skills - She studied programming at a Ukrainian university before immigrating to the United States. - Although she understood written English and knew how to program, communicating in English was initially more difficult than learning programming languages. - She took ESL classes while studying C++ and Java through Berkeley Extension. - Her first job was in hardware diagnostics at a five-person company. - A language barrier caused her to run out of time on a technical interview, but an interviewer familiar with her Berkeley class gave her another opportunity. - She eventually transitioned from C++ to Java, which became her primary language for many years. ## Moving Down the Stack and Into Leadership - Sulkina’s career progressed from hardware diagnostics to frontend, backend, and infrastructure engineering. - At the same time, she increasingly took on leadership responsibilities. - At Caymas Systems, her manager recognized her leadership potential and showed her the difference effective leadership makes. - At Comcast, she moved from individual contributor to engineering manager. - Coaching engineers, building software collaboratively, and developing high-performing teams convinced her that leadership was the right path. ## Lessons from Twitter’s Distributed Systems - During nearly nine years at Twitter, Sulkina advanced from first-line manager to director. - She worked through major operational events, including the “fail whale” period and the tweetstorm surrounding Ellen DeGeneres’s viral selfie. - Twitter’s transition from a monolith to microservices taught her that failure is inevitable in complex systems. - Resilient distributed systems must be designed to handle failures rather than assuming failures can be prevented. - Her cultural lesson involved turning promising ideas into adopted technologies. - She helped bootstrap Twitter’s GraphQL API, replacing legacy REST services. - The effort required leadership support, cross-team consensus, and stakeholder alignment, but ultimately improved product teams’ development velocity. ## Choosing Airbnb - Airbnb contacted Sulkina in 2022, when she felt ready to move beyond a well-established organization at Twitter. - The company appealed to her because it combined her professional interests with her personal passion for travel; she had been an Airbnb guest since 2013. - Airbnb’s Developer Platform organization had strong work happening in separate silos but needed clearer strategy, direction, and trust across engineering. - Sulkina began by clarifying the organization’s purpose and future direction. - Her early priorities included strengthening the organization, coaching leaders, and creating alignment within the team and with the teams it supported. - Over the following years, this work produced a high-performing organization with clearer strategy, stronger execution, and a focus on delivering business value. Sulkina’s story emphasizes that technical growth, organizational leadership, and personal motivation can reinforce one another. Her experience suggests that successful engineering leaders design for failure, invest in alignment, and use clear strategy to turn fragmented efforts into meaningful platform-wide impact.

javadistributed-systemsmicroservicesgraphql+3

airbnbAug 13, 2025

Migrating Airbnb’s JVM Monorepo to Bazel (opens in new tab)

Airbnb migrated its tens-of-millions-of-lines JVM monorepo from Gradle to Bazel over 4.5 years, achieving faster builds, testing, IntelliJ syncs, and development deployments. The move was driven by Bazel’s scalable remote execution, hermetic builds, and ability to provide shared infrastructure across Airbnb’s language-specific repositories. A gradual rollout, extensive automation, and close collaboration with service teams were central to making the migration successful. ## Results of the Migration - Build CSAT increased from 38% to 68%. - Local build and test times became 3–5 times faster. - IntelliJ syncs became 2–3 times faster. - Development-environment deployments became 2–3 times faster. ## Why Airbnb Chose Bazel ### Faster Builds Through Remote Execution - Large Gradle builds frequently took more than 20 minutes locally, while pre-merge CI builds had a p90 of 35 minutes. - Gradle had already been optimized with powerful machines and build sharding, but sharding caused underutilization and duplicated shared work. - Bazel’s cacheable actions and remote build execution enabled thousands of actions to run in parallel on short-lived workers. - “Build without the Bytes” reduced the amount of build output developers needed to download. - Bazel analysis runs in parallel, unlike the often single-threaded configuration phase of large Gradle projects. - Remote execution also improved local build performance, not just CI performance. ### More Reliable and Reproducible Builds - Gradle tasks could access the entire filesystem, creating accidental dependencies and race conditions. - Bazel sandboxes expose only declared inputs to each action, preventing undeclared files from affecting builds. - Bazel’s remote execution runs actions in identical containers with strict resource limits. - Using remote execution for both local and CI builds reduced differences between developer and CI environments. ### A Shared Build Infrastructure Layer Because Airbnb’s web, iOS, Python, Go, and JVM repositories all use Bazel, the company could standardize infrastructure for: - Remote caching - Remote build execution - Affected-target calculation - Build Event Protocol instrumentation and logging ## Starting with a Proof of Concept - Airbnb first migrated Viaduct, a large GraphQL monolith platform. - Viaduct was selected because it was complex, had slow builds, affected roughly 300 product engineers monthly, and had an infrastructure team willing to collaborate. - Bazel and Gradle initially coexisted, allowing developers to choose either system. - The team ported Viaduct’s build logic and created an automated Bazel build-file generator because the Gradle dependency graph continued to change. - Although Bazel was initially 2–4 times faster locally, developers did not adopt it immediately. - The team spent several additional months fixing missing integrations and bugs before Viaduct engineers voluntarily switched. ## Scaling Across the JVM Monorepo - Airbnb expanded breadth-first, aiming to make the entire repository compile and test under Bazel. - Gradle and Bazel continued to coexist during the migration. - This allowed developers to use Bazel locally while deployments still relied on Gradle. - Gradle provided a fallback when Bazel infrastructure, such as remote caching or execution, experienced incidents. - Maintaining two build graphs was costly, so Airbnb invested heavily in automation rather than requiring developers to maintain Bazel files manually. ## Automated Build-File Generation - The generator was inspired by Gazelle but was built internally to meet stricter performance requirements and handle dependency cycles. - It parses Java, Kotlin, and Scala source files to identify packages, imports, and symbol declarations. - These relationships are used to construct a file-level dependency graph. - Since generation ran on every commit before merging, Airbnb added external caching to keep it fast. - CI publishes a cached repository index for each mainline commit, allowing the generator to rescan only directories changed since that commit. Airbnb’s experience suggests that a large build-system migration is most effective when introduced incrementally: prove the benefits on a representative service, automate maintenance, preserve a fallback during rollout, and address developer workflow issues before expanding across the organization.

javakotlinmonorepojvm+4