Aimane Benammar

– Developed and maintained critical business services in Java (Spring Boot) and C# (.NET), applying strict quality practices, unit and integration testing, code reviews, and SOLID/Clean Code standards to ensure readability, maintainability, and robustness. – Designed REST APIs at Henner with clear, long lasting contracts, documented with OpenAPI/Swagger, secured with OAuth2/JWT, and built to cover key business needs. – Improved service observability to support operations: structured logging, error tracking, technical and functional metrics, and tracing when available, speeding up incident diagnosis and improving reliability. – Worked on data performance and stability (DB2 & SQL Server): query optimisation, appropriate indexing, execution plan analysis, tuning aligned with business constraints (latency/volume), and secure access (transactions, consistency, error handling). – Increased reliability of integrations with COBOL batch processing: secured exchanges, rejection handling, restart/recovery mechanisms, job monitoring, and end-to-end flow maintenance in constrained environments (deadlines, criticality, dependencies). – Containerised applications with Docker and contributed to deployments on Kubernetes and OpenShift, managing Helm configurations, secrets, liveness/readiness probes, and HPA autoscaling to ensure performance and availability. – Industrialised delivery with GitLab CI and Jenkins by setting up build and test pipelines, quality gates, SAST/DAST security scans, dependency analysis, and automated multi environment deployments to improve release speed and reliability.

– Développé des applications data et métier en C#, WPF, JavaScript, Python et SQL pour le monitoring, le reporting, l'analyse d'incidents et l'aide à la décision opérationnelle. – Construit et maintenu des flux ETL et d'intégration avec Talend, Python, DB2 et SQL Server, transformant des données brutes en datasets fiables, exploitables et documentés. – Optimisé les couches data DB2 et SQL Server : requêtes critiques, index, procédures stockées, plans d'exécution, tuning SQL et réduction de la latence sur traitements volumineux. – Défini des KPI d'exploitation : taux d'erreur, durée de batch, disponibilité, volumétrie, et mis en place un alerting bout-en-bout pour sécuriser les pipelines COBOL critiques. – Industrialisé les livraisons avec GitLab CI, Jenkins, Docker, Kubernetes/ OpenShift et pratiques CI/CD afin de fiabiliser les déploiements automatisés et répétables.