Mike Seddon is a Principal Engineer specializing in Generative AI with 13 years of experience designing and shipping high-throughput, auditable data platforms and production ML systems. He combines hands-on engineering—rewriting services for simplicity, building Rust/GStreamer GPU pipelines that processed tens of billions of frames, and leading the reimplementation of CBA's Generative AI guardrails—with architecture-level thinking around transaction guarantees, immutability and multi-cloud portability. A pragmatic polyglot, he has rebuilt systems from GraphQL/Azure Functions to single-container TypeScript services and driven SQL-first ETL frameworks moving billions of records daily. Mike is also an active open-source contributor to Apache Arrow/DataFusion (notably improving SQL/function support and Parquet statistics), showing deep backend and data-engineering expertise. Based in Greater Sydney, he blends startup founding experience with enterprise delivery, and favors simplicity, strong static analysis and extensible plugin-driven designs. An unusual strength: he pairs production-scale data engineering rigor with low-level performance work in Rust to squeeze real-world systems for both correctness and speed.
13 years of coding experience
14 years of employment as a software developer
Business Information Systems Information Systems, Business Information Systems Information Systems at RMIT University
Apache Arrow is the universal columnar format and multi-language toolbox for fast data interchange and in-memory analytics
Role in this project:
Back-end Developer
Contributions:128 reviews, 23 commits, 34 PRs in 4 months
Contributions summary:Mike primarily contributed to the DataFusion component of the Apache Arrow project, focusing on implementing and enhancing SQL functionality. Their work includes adding support for the `BETWEEN` operator, implementing various string functions (e.g., `ascii`, `chr`, `initcap`, `repeat`, `reverse`, `to_hex`), and optimizing casting operations, particularly UTF8 to Date32 conversion. They also addressed issues with RepartitionExec and added feature flags for dependencies to improve the project's modularity. Their contributions also covered implementing statistics for ParquetTableProvider.
Contributions:17 reviews, 3 commits, 4 PRs in 1 month
Contributions summary:Mike made significant contributions to the Apache DataFusion SQL Query Engine, primarily focusing on enhancing its capabilities and functionality. Their work involved implementing the `BETWEEN` operator, adding support for `TypedString` and `DATE` coercion, and implementing several string functions. Furthermore, the user implemented statistics for Parquet table providers and added the ability to load and parse expected query answers to test the correctness of the queries.
querypythonquery-enginedataframerust
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