Barak Michener
Senior Database Engineer
Berkeley, California, United States
Summary
🤩
Rockstar🎓
Top SchoolBarak Michener is a Senior Database Engineer in Berkeley with 16 years building reliable distributed systems, databases, and back-end infrastructure across startups and major tech firms. He has led projects from skunkworks prototypes to production teams (notably at CoreOS on Torus) and shipped data-centric features at Lyft, Google, and recent roles including OpenAI and AuthZed. Barak is an active open-source contributor to high-profile projects like etcd and Ray, improving core server reliability, configuration, retry/backoff logic, build and release processes, and iterator performance in graph databases. He combines deep systems engineering—storage, consensus, and distributed runtime—with pragmatic DevOps and testing discipline to reduce operational surprises. Known for quietly cleaning up cruft and stabilizing builds and configs, he brings both hands-on coding and architectural sense to complex data problems. Trained in EECS at UC Berkeley, he favors measurable improvements (tests, sanity checks, dependency centralization) that make systems easier to maintain and operate.
16 years of coding experience
15 years of employment as a software developer
BS EECS, BS EECS at University of California, BerkeleyBear River
English, French
Github Skills (30)
algorithm10
algorithms10
docker10
key-value-store10
python10
testing10
distributed-systems10
build-system10
graph-database10
etcd10
dockers10
data-structure10
cicd10
go10
data-structures10
Programming languages (14)
Github contributions (5)
cayleygraph/cayley
Jun 2014 - Sep 2018
An open-source graph database
Role in this project:
Back-end Developer
Back-end DeveloperContributions:3 releases, 430 commits, 169 PRs in 4 years 3 months
Contributions summary:Barak's commits focus on the implementation of data structures and iterator optimization within the open-source graph database. The user removed unnecessary goroutines from the iterator, improved the iterator's clean-up, and fixed a REPL-clearing-state bug. Further contributions include the addition of logging, utilizing all processors, and a fix for an indexing crash within the graph database.
cayleylinked-datadatabasegraphgraph-database
ray-project/ray
Jul 2020 - Mar 2021
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:1 release, 68 reviews, 54 commits in 7 months
Contributions summary:Barak primarily contributed to improving the build and deployment processes. This includes centralizing and unifying dependency versions in `requirements.txt` files, improving the parsing of the Bazel version, and creating and updating Docker images. They also fixed several testing and build issues, and improved the release process by bumping the version number. Further contributions are visible in updating the python, protobuf and java build, as well as refactoring of the client.
pythonconsistsruntimetensorflowserving
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