Daniel Mills - Co-Founder at Cambra

Daniel Mills

Co-Founder at Cambra

Seattle, Washington, United States

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Summary

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Rockstar

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Top School

Daniel Mills is a seasoned back-end software engineer and founder with 16 years building large-scale data processing systems, now co-founding Cambra in Seattle. He spent a decade at Google and five years at Snowflake, driving core work on distributed pipeline SDKs and runtime behaviors. An active open-source contributor to flagship projects like Apache Beam and the Google Cloud Dataflow Java SDK, he has focused on windowing, side outputs, timers, and robust testing for streaming and batch processing. He combines deep practical experience in stateful ParDo semantics and coder refactors with a strong testing-first mindset that improves reliability at scale. Daniel holds MS and BS degrees from the University of Washington in computer science and mathematics, and often focuses on subtle correctness issues—like large-key handling and state access semantics—that many engineers overlook.

16 years of coding experience

14 years of employment as a software developer

Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at University of Washington

Github Skills (17)

testing10

test-framework10

java10

javas10

data-processing10

windowing10

apache-beam10

software-quality9

data-structures8

protobuf8

protobuffer8

data-structure8

big-data8

go4

python4

Programming languages (3)

JavaRubyPython

Github contributions (5)

GoogleCloudPlatform/DataflowJavaSDK

Dec 2014 - Nov 2016

Google Cloud Dataflow provides a simple, powerful model for building both batch and streaming parallel data processing pipelines.

Role in this project:

Back-end Developer

Contributions:113 commits, 4 PRs, 2 comments in 1 year 11 months

Contributions summary:Daniel primarily contributed to the core Dataflow Java SDK by modifying and refactoring existing Java code related to windowing and side output functionalities. The user removed type parameters from existing code and added features like DoFn.Context.sideOutputWithTimestamp, and adjusted the handling of timers. Furthermore, the user updated the codebase to incorporate new functionalities for unbounded sources and improved testing.

stream-processingbeambatchdata-processingparallel

apache/beam

Mar 2016 - Feb 2020

Apache Beam is a unified programming model for Batch and Streaming data processing.

Role in this project:

Back-end Developer & Test Automation Engineer

Contributions:8 reviews, 14 commits, 17 PRs in 4 years

Contributions summary:Daniel primarily contributed to the Apache Beam Java SDK, focusing on enhancing the testing framework and improving code quality. Their work included adding tests for large keys within the `GroupByKeyTest`, specifically testing with varying key sizes to ensure robust functionality. Additionally, they refactored and updated the `AuctionOrBidWindowCoder`, addressing consistency and structural value issues. This involved modifications to state access semantics in the direct runner and optimizing the display of display data for stateful ParDo transforms.

golangpythonstreaming-databeambatch

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