Data Engineer specializing in cloud-native architectures and analytics infrastructure
I'm a Software Architect and Senior Developer with over two decades of experience across the complete software lifecycle, seeking a hands-on engineering role. My expertise spans architecting, building, and scaling complex cloud-native systems in demanding sectors.
Currently serving as Software Architect at ENGIE, I co-lead architectural strategy for C3NTINEL, a global energy analytics platform. I specialize in designing resilient microservices architectures using Java and Spring Boot on AWS, with extensive experience in DevOps, data engineering, and event-driven processing. My work includes migrating on-premises systems to the cloud, building time-series calculation frameworks, and developing customizable dashboards.
As an AWS Certified Solutions Architect with a PhD in Physics, I bring analytical rigor to technical challenges. I'm passionate about lean/agile methodologies (Scrum Master, XP advocate), implementing CI/CD pipelines, and building maintainable systems that scale. My background includes diverse experience across finance, energy, transport, retail, and government sectors.
Associate Level
Amazon Web Services
Foundational Level
Amazon Web Services
Led end-to-end architectural strategy and full-stack development of C3NTINEL, a comprehensive energy analytics platform serving global users at ENGIE. Engineered an AWS-centric microservices architecture using Java 11 and Spring Boot, managed via Elastic Beanstalk and Infrastructure-as-Code (CloudFormation). Architected a flexible data framework for complex time-series data visualization across multiple time zones with SI/non-SI unit support. Established maintainable CI/CD pipelines (Jenkins, CodePipeline, GitHub Actions) and developed event-driven data processing backends using ActiveMQ, Lambda, and Glue. As Scrum Master, championed Extreme Programming (XP) practices and established a robust testing pyramid strategy, including TDD/BDD with Cucumber.
Interested in working together or discussing data engineering challenges? I'd love to hear from you.