Daniel Walters is a data engineer with 11 years’ experience building scalable data pipelines and cloud infrastructure across GCP and AWS, currently driving large-scale projects at OnBuy such as an LLM-powered product categorisation of 18M SKUs and a Price Runner pipeline processing 7M+ products. He combines strong Python, Airflow and Terraform expertise with hands-on optimisation—e.g., cutting an ETL runtime from 8+ hours to under 2 via async Python and Cloud Run—and has led migrations that eliminated downtime for stakeholder reports. A chemist by training (First Class BSc, MPhil), Daniel brings a researcher's attention to data quality and automation, having implemented finance automations and GDPR-compliant data removal services. He also contributes to embedded C++/IoT open-source work (notably improvements to the Sming framework), reflecting a breadth from low-level firmware to cloud-native analytics.
11 years of coding experience
4 years of employment as a software developer
Master of Philosophy - MPhil Chemistry, Master of Philosophy - MPhil Chemistry at The University of Manchester
Bachelor of Science - BSc Chemistry, Bachelor of Science - BSc Chemistry at University of Nottingham
Sming - powerful open source framework simplifying the creation of embedded C++ applications.
Role in this project:
Embedded Systems Engineer / IoT Developer
Contributions:22 commits, 3 PRs, 31 comments in 2 months
Contributions summary:Daniel primarily focused on enhancing the Sming framework for embedded C++ applications. Their contributions include implementing MQTT unsubscribe functionality, incorporating the use of `lwip` instead of `openlwip`, and fixing RTC-related issues. Furthermore, the user has addressed serial communication and WS2812 library integration. The commits demonstrate a clear understanding of the Sming framework and its underlying hardware.
Contributions:6 PRs, 18 pushes, 2 branches in 2 years
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