Damien Saillard
Coach Devops - Expert En Méthodes Et Outils at Société Générale
Paris, Ile-de-France
Summary
👤
Senior🎓
Top SchoolDamien Saillard is an experienced software engineer and DevOps coach with over a decade of hands-on practice, currently driving Cloud@Scale transformation at Société Générale. He blends deep development roots (PHP, Node.js, modern JS frameworks) with strong cloud and platform expertise—Kubernetes/Openshift, Docker, Terraform, Ansible—and a lasting focus on security, reliability and efficiency. Damien has led and scaled DevOps teams, standardized application estates, and introduced CI/CD and observability across complex banking environments. He also contributes to prominent open-source projects like Mongoose, where he fixed subtle discriminator-model bugs that improved model hydration and query correctness. Known for methodical organization and a collaborative coaching style, he pairs technical rigor with pragmatic proposals that move large programs forward.
13 years of coding experience
15 years of employment as a software developer
Baccalauréat STI Mécanique, Micro-technique, Mention Très Bien, Baccalauréat STI Mécanique, Micro-technique, Mention Très Bien at Lycée Jules Haag de BesançonCPGE, Mathématiques, Physique, Mécanique, Electrotechnique, CPGE, Mathématiques, Physique, Mécanique, Electrotechnique at Classe préparatoire scientifique TSI au lycée Jules Viette de MontbéliardDiplôme d'ingénieur, Ingénierie des Systèmes Automatiques et Vision, Diplôme d'ingénieur, Ingénierie des Systèmes Automatiques et Vision at Télécom Physique Strasbourg
English
Github Skills (7)
mongoose10
javascript10
nodejs10
odm10
mongodb10
mongodb-database10
testing10
Programming languages (2)
Github contributions (5)
Automattic/mongoose
Dec 2013 - Jan 2014
MongoDB object modeling designed to work in an asynchronous environment.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:10 commits in 1 month
Contributions summary:Damien focused on resolving issues related to discriminator models within the Mongoose library. They fixed several bugs related to finding documents with discriminators when field selection was used, ensuring correct model hydration and preventing incorrect results. The contributions involved modifying the core `lib/model.js` and adding and modifying tests in `test/model.discriminator.querying.test.js` and `test/query.test.js` to cover different scenarios. These changes improved the accuracy and reliability of the Mongoose library when working with discriminator models.
pythonasynchronousmongoodmmodeling
daemon1981/mongoose-rattle-plugin
Oct 2013 - Oct 2015
Contributions:66 commits, 1 PR, 1 push in 1 year 11 months
mongointeractionsmongoosemongodbsocial-interactions
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