Matthew Ho
Software Engineer at Netflix
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolMatthew Ho is a software engineer with 8 years of experience specializing in data infrastructure and distributed systems, currently building low-latency key-value abstractions at Netflix. He has driven PB-scale real-time ingestion pipelines at LinkedIn, improving data freshness from hours to minutes and owning lifecycle features like compaction and retention for lakehouse data. His background spans high-availability database frameworks at Okta and Microsoft, plus research work accelerating Hyperledger Fabric via optimistic concurrency control. An active open-source contributor to Apache Gobblin, he has tackled core ingestion semantics, race conditions, and test coverage for a widely used data integration framework. Based in New York, he blends production-grade engineering with a penchant for elegant solutions—aptly hinted by his GitHub bio’s playful “al dente spaghetti” metaphor for resilient, well-structured code.
8 years of coding experience
5 years of employment as a software developer
Bachelor of Science Computer Science, Bachelor of Science Computer Science at UC Santa Barbara
Github Skills (8)
javas10
data-pipelines10
data-pipeline10
back-end-development10
data-integration10
java10
unit-testing8
helix8
Programming languages (7)
Github contributions (5)
apache/gobblin
Apr 2022 - Jan 2023
A distributed data integration framework that simplifies common aspects of big data integration such as data ingestion, replication, organization and lifecycle management for both streaming and batch data ecosystems.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:266 reviews, 13 commits, 49 PRs in 9 months
Contributions summary:Matthew primarily contributed to the core functionality of the Gobblin data integration framework. Their commits focused on enhancing the GMCE schema, addressing race conditions, and implementing improvements to the code's test coverage. The user also worked on fixing compaction actions on job failures, which involved refining the verification of time ranges within the framework. In addition, the user was involved in handling null value edge cases when querying Helix.
datadcosdata-streambig-data-integrationbatch-data
homatthew/gobblin
Feb 2022 - Apr 2024
A distributed data integration framework that simplifies common aspects of big data integration such as data ingestion, replication, organization and lifecycle management for both streaming and batch data ecosystems.
Contributions:19 reviews, 20 PRs, 352 pushes in 2 years 2 months
big-data-integrationbatch-dataecosystemsaspectsdata-ingestion
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