Freelancer profile translated to English.
Back to original languageAbout Anouar
Senior Developer | Java · Spring Boot · Cloud-Native Architecture
Using Claude Code, I employ a Spec Driven Development (SDD) methodology based on nwave.ai.
I support my clients in the design, redesign, and deployment of robust and scalable applications — from scoping to production, with a high level of requirement on quality and performance.
⚙️ Java Development · Spring Boot · Node.js · Rust (in progress)
🧱 Microservices Architecture · DDD · Event Sourcing · CQRS · Kafka · MongoDB Audit and refactoring of Legacy applications.
☁️ DevOps & Cloud Kubernetes · Terraform · Docker · Helm · ArgoCD · GitLab CI · Jenkins
📊 Data & Performance Kafka Streams · Spark · Scala · SQL — real-time processing & high-traffic optimization.
🧑💻 Recent Missions
🏦 Crédit Agricole CIB
Microservices redesign of a securitization application
Java 21, Spring Boot, Kafka, K8s, React.js
🛡️ MAIF
Technical Lead — Business Microservice & API
Java, Spring Boot, Kafka, K8s, React.js
🌍 Allianz Trade
Public REST API on Legacy system
AWS Lambda, Node.js, Elasticsearch, Terraform
🧭 A project? Let's talk. I am available for your technical and strategic challenges. 🚀
French
Native or bilingual
English
Fluent
Remote only
Primarily works remotely
Experience
- Crédit Agricole-CIBTech Lead Java | Microservices | KafkaBANKING AND INSURANCEOctober 2023 - Today (2 years and 8 months)Paris, FranceDevelopment of a securitization applicationContext: Monolithic Legacy Application, securitization business managed using Excel spreadsheets and file exchanges with clients, business opportunity identified.Technical Leadership
- Implementation and support of an SDD methodology based on BMAD
- Analysis and breakdown of the business domain bounding contexts according to DDD patterns
- Development of a calculation engine for financial flows, triggers, controls, and indicators
- Implementation of patterns: outbox pattern, tactical patterns
- Leadership of MVP development (**Java** /Spring Boot/Kafka)
- Hexagonal architecture / Clean Architecture
- Development of a ReactJS / Redux front-end
- Ensure application run, monitoring, production environment management
- Support and training for developers and Business Analysts on writing User Stories
- Awareness on Unit Tests and BDD practices (**Cucumber**, **Gherkin**)
Microservices Architecture / Event Driven- I ensure compliance with the principles of Hexagonal Architecture / Clean Architecture / SOLID
- I defined the messaging strategy between micro-services; event-driven in choreography mode via Kafka topics
- Implementation of the Outbox pattern for double MongoDB/Kafka writing
- I implement the technical monitoring stack: I collect the necessary metrics for application monitoring in production using **micrometer**, **prometheus**, dashboard creation (**Grafana**)
DevOps- I implemented the CI/CD pipeline on Gitlab /
- GitOps pattern with ArgoCD
- I wrote the Kubernetes infrastructure in Helm for the different environments onKubernetes**, **docker**, **helm**, **Vault, kaniko, terraform, AWS EKS
=> modern scalable application, smoother Deal management=> allowed my client to accept more complex Deals - BpifranceTech Lead Java | Microservices | KafkaPUBLIC SECTOROctober 2022 - September 2023 (11 months)Counterparty Risks: Calculate the Performance Status of counterpartiesin real-timevia trigger eventsMicroservices Architecture / Event Driven▪ Team Formation▪ Bounded Context breakdown following DDD patterns▪ Microservices development in hexagonal architecture▪ Event-driven communication via Kafka topics in choreography mode / outbox pattern▪ Observability implementation: Micrometer Prometheus metrics▪ Spring boot /Java/ Spring Cloud / Kafka / Kubernetes / MongoDB / Micrometer / DatadogBig Data▪ Progressive transition from a Legacy monolith to a microservices architecture▪Kafka Connectimplementation to broadcast a stream from a Legacy database▪ Definition ofKafka Streamstopologies onConfluent▪ Implementation of joins and aggregations necessary to calculate counterparty performance status▪ Development ofGlue PySpark/ Spark SQL jobs for event aggregation▪ Data streamer: comparison of data and generation of deltas in event format▪ Automatic triggering of Spark jobs upon detection of Data files: S3 events/ AWS Lambda / Step Function ▪ AWS Glue / Spark / Python / PySpark / SparkSQLTechnical Leadership▪ Scoping and technical design of topics▪ Developer support and code review▪ Infrastructure cost optimization▪ Maintenance and implementation of NFRsDEVOPS▪ Infrastructure as Code with Terraform▪ CI/CD implementation for microservices Jenkins/ Flux CD▪ Creation of microservices infrastructure: AWS EKS, Docker, Kubernetes, HelmAgility▪ SCRUM / SAFE methodology▪ Pair programming▪ Code review and Merge Request validation▪ Test automation via Jira Xray/ Gherkin / Karate DSL
- MAIFLead Dev | Java | Spring Boot | Micro Services | Event DrivenBANKING AND INSURANCEDecember 2019 - September 2022 (2 years and 10 months)Technical leadership of a Squad of 5 Developers, 1 PO, 2 BAs, and 1 Tester•Development of internal MAIF Micro-Services•Implementation of an event-driven architecture via Kafka•Development of micro front-end React / ReduxMicroservices Architecture
- Development of a REST API inJava
- Development of a front-end and BFF
- Implementation of centralized configuration Spring Cloud Config
- SSO implementation via PingFederate
- MTLS implementation with various components, **PostgreSQL**, Kafka, and PingFederate
Event Driven- Communication with other internal micro-services via Kafka notifications
Technical Leadership- Hexagonal Application Architecture
- Developer support
- Implementation of Craft practices: Clean Code, TDD
- Pull Request validation
- Maintenance of the Squad's Application portfolio
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Education
- MACHINE LEARNING AND ARTIFICIAL INTELLIGENCESorbonne University2022Mettre en œuvre la science des données et l’apprentissage profond au service de l’intelligence artificielle . Module 1 : Outils de Mathématiques et d’informatique Module 2 : Apprentissage statistique supervisé Module 3 : Apprentissage non-supervisé et apprentissage profond Module 4 : Apprentissage profond et réseaux de neurones pour les images Module 5 : Réseaux de neurones pour le traitement du langage et environnement des données Module 6 : Cloud Computing et Big Data Module 7 : Applications (et déclinaisons par secteur d’activité) Module 8 : Projet Data
- Engineering degree in Computer Science and MultimediaHigher Institute of Computer Science and Multimedia2011