Thomas Groh
Software Engineer at Stripe
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolThomas Groh is a Senior Software Engineer and technical lead with 11 years of experience building reliable, large-scale data processing and SRE tooling, now at Stripe in Seattle. He has led a six-person engineering productivity team at Google, driving company-wide deployment and release improvements while mentoring junior engineers and creating onboarding materials. As an Apache Beam committer and contributor to Google Cloud Dataflow, he implemented core runtime and testing primitives that made deterministic testing and local prototyping of streaming pipelines possible. His background spans production SRE roles (Waze, GKE/App Engine) where he halved debug log ingestion costs and expanded SLO coverage, demonstrating a strong blend of operational rigor and developer-focused infrastructure. Equally comfortable in open-source and corporate settings, he focuses on reducing toil and improving developer velocity through pragmatic tooling and API-first design.
11 years of coding experience
11 years of employment as a software developer
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at Stony Brook University
Stackoverflow
511reputation
13kreached
9answers
1question
Github Skills (18)
dataflow-programming10
dataflow10
testing10
java10
javas10
data-processing10
apache-beam10
unit-testing9
pipe9
pipeline9
junit9
state-management9
eclipse6
integration-testing6
google-cloud-storage6
Programming languages (3)
Github contributions (5)
apache/beam
Feb 2016 - Jun 2018
Apache Beam is a unified programming model for Batch and Streaming data processing.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:1266 commits, 803 PRs, 147 pushes in 2 years 3 months
Contributions summary:Thomas's contributions primarily focused on improving the Apache Beam project's core functionality related to data processing, specifically in ensuring the outputs of Read transformations are consumed in Dataflow. They implemented a "no-op ParDo" transformation to consume unconsumed read outputs and also worked on handling specific types of processing such as GroupBy operations that required additional attention. Their work included adding methods, handling specific cases in various processing steps, and contributing to the testing framework to ensure these changes functioned correctly.
golangpythonstreaming-databeambatch
GoogleCloudPlatform/DataflowJavaSDK
Feb 2015 - Nov 2017
Google Cloud Dataflow provides a simple, powerful model for building both batch and streaming parallel data processing pipelines.
Role in this project:Backend Developer
Backend DeveloperContributions:191 commits, 86 PRs, 6 pushes in 2 years 9 months
Contributions summary:Thomas primarily focused on improving and testing the core logic of the Java SDK, with a strong emphasis on internal APIs. They implemented new methods to obtain and manage the state of running pipelines and made updates to Dataflow and Direct runners, to utilize the new features. Further contributions were in the form of unit tests for ensuring correct behavior of key SDK methods such as the addition of counters, and also ensured the correct behavior of internal state management, while maintaining backwards compatibility.
stream-processingbeambatchdata-processingparallel
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