Javier Ramirez is a developer advocate and developer relations lead with over 17 years of hands-on software and cloud experience and 20+ years in IT, specializing in cloud architecture and data engineering. A long-time Google Developer Expert and GCP Authorized Trainer, he’s Google-certified as both Cloud Architect and Data Engineer and regularly consulted on complex data migrations. He blends deep expertise across SQL, NoSQL, graph, in-memory and time-series systems with early back-end roots in distributed systems and web apps, and contributes upstream to notable open-source projects like QuestDB to enhance time-series SQL capabilities. Javier pairs technical leadership—having founded multiple companies and led engineering teams—with extensive public-facing work: delivering trainings, university lectures, and more than 60 conference talks across 15 countries. He brings a startup mindset to large-enterprise problems and a genuine enthusiasm for teaching and community-building, from kids’ coding platforms to enterprise cloud migrations.
17 years of coding experience
20 years of employment as a software developer
BSc (1st Class Hons.), Business Information Systems, BSc (1st Class Hons.), Business Information Systems at Cardiff University / Prifysgol Caerdydd
Ingeniería Informática, 1996, Ingeniería Informática, 1996 at Universidad Fundación San Valero, Zaragoza, Spain
QuestDB is a high performance, open-source, time-series database
Role in this project:
Back-end Developer / Database Engineer
Contributions:33 reviews, 11 PRs, 54 pushes in 4 years 3 months
Contributions summary:Javier contributed to the QuestDB project by implementing new SQL functions, specifically `pg_catalog.version()` and `nullif(long, long)`. Their work involved modifying existing Java code within the core modules, including the addition of new classes, interfaces, and test cases. Furthermore, they added support for milliseconds and microseconds to `datediff` and `dateadd` SQL functions, indicating a focus on enhancing the database's time-series capabilities.
Template to quickstart streaming analytics using Apache Kafka for ingestion, QuestDB for time-series storage and analytics, Grafana for near real-time dashboards, and Jupyter Notebook for data science
Contributions:134 pushes, 1 branch in 1 year 4 months
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