Miguel Arregui is a Senior Software Engineer with nine years focused on building high-performance, fault-tolerant backend systems where failure is expensive and performance is non-negotiable. He has deep expertise in Java-based distributed databases and time-series systems, contributing production-grade features to notable open-source projects like CrateDB and QuestDB (including SQL scalar functions, geohash support, and time-zone handling). At QuestDB he helped design core SQL features and implemented an async cloud provisioning backend in Python, showing fluency across JVM and Python ecosystems and operational tooling (Kafka, Kubernetes, Postgres). Currently at HiveMQ, he focuses on scalability, availability and efficient delivery for massive IIoT traffic, reflecting a long track record in low-latency, high-throughput financial and industrial systems. His background spans mission-critical domains from trading platforms and scientific satellite operations to enterprise bioinformatics, demonstrating a rare mix of systems thinking and pragmatic delivery. Colleagues describe him as an engineer who not only optimises performance but anticipates production edge-cases that others often overlook.
10 years of coding experience
19 years of employment as a software developer
B.Sc + M.Sc rolled into one program of 4000 hours Computer Science and Engineering, B.Sc + M.Sc rolled into one program of 4000 hours Computer Science and Engineering at Universitat Jaume I
QuestDB is a high performance, open-source, time-series database
Role in this project:
Back-end Developer & Database Engineer
Contributions:435 reviews, 294 commits, 67 PRs in 1 year 6 months
Contributions summary:Miguel primarily focused on enhancing the SQL capabilities of QuestDB, a time-series database. Their contributions included implementing support for `NULL` as a variant type, adding trigonometric functions, and improving geohash handling. They also worked on improving the "SHOW" commands for ACL and partitions while fixing various SQL related problems, such as incorrect execution of SQL with multiple latest by columns and fixing code causing flakiness.
CrateDB is a distributed and scalable SQL database for storing and analyzing massive amounts of data in near real-time, even with complex queries. It is PostgreSQL-compatible, and based on Lucene.
Role in this project:
Back-end Developer
Contributions:1 review, 45 commits, 84 PRs in 1 year 2 months
Contributions summary:Miguel implemented several SQL scalar functions related to string manipulation and time zones, enhancing the capabilities of the CrateDB database. These additions included the `left`, `right` and `trunc` functions for string truncation and formatting, and the `timezone` and `pg_get_function_result` functions for time zone conversions. Furthermore, the user contributed to the core functionality of the database by integrating JOIN USING statements and creating functions like `generate_subscripts`.
cratedblucenepostgresqlsql-databasesql
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