Data Engineer

Profile

A graduate information systems engineer from EPISEN, I spent two work-study years at Crédit Coopératif – Groupe BPCE as a full-stack & data developer: Angular interfaces, Java / Spring services and REST APIs, automated tests and Power BI dashboards, from requirements to run. Over a three-year team engineering project, I helped evolve a platform into Spring Boot and React / TypeScript microservices with Kafka and WebSocket messaging, and a data pipeline built on Kafka, Spark and Airflow.

Professional experience

Crédit Coopératif – Groupe BPCE · Paris · IT Studies and Data department, Java team

• Automated checks and workflows with Power Automate, and built Power BI dashboards on quality, vulnerabilities and progress.
• Gathered and analysed user needs, formalised business rules and contributed to the design choices of three internal applications.
• Built Angular interfaces and Java / Spring services, with REST APIs, data persistence and error handling.
• Industrialised Jenkins CI/CD pipelines for Java / Angular projects: artifacts in Artifactory, automated tests, SonarQube and Checkmarx scans, deployments with XL Deploy.
• Supported the run: defect tracking, reproduction, first-level diagnosis and coordination with users, IT teams and production.

Highlights

SIRIUS engineering project

EPISEN · 2023 – 2026 · team of 3

• Data pipeline: Python collectors (Bluesky, Nostr, Hacker News, RSS, Stack Overflow), Kafka and Redis de-duplication, MinIO raw zone, Spark transformations orchestrated by Airflow, indicators served by PostgreSQL, Streamlit and Power BI reporting.
• Three-year evolution: JavaFX desktop client and monolith, then Angular and Maven, then Spring Boot and React / TypeScript microservices behind an Express BFF.
• Real time and events: WebSocket, Kafka and Redis Pub/Sub between instances; polyglot persistence with PostgreSQL, MongoDB, Redis and MinIO.
• GitOps delivery with GitHub Actions and Argo CD; UML modelling and agile practices across all three years.

Engineering thesis

• Controlled protocol: 10 Kubernetes incident scenarios, 15 repetitions, 2 models and 3 architectures (LLM only, RAG, ReAct agent), for 900 trials blind-scored by 2 LLM judges.
• With Kimi K2.5, the ReAct agent reaches 5.113/6 versus 4.230/6 for classic RAG, at the cost of about 77.84 s of extra latency and about 34,258 extra tokens.
• Recommendation: classic RAG by default (4.120/6 with Qwen in about 4.04 s), agentic escalation for complex incidents. FastAPI service tested, containerised and deployed with GitHub Actions and Argo CD.

Education

Engagement

• Maths tutor with Acadomia since 2023: 8 middle and high school students supported with tailored explanations, targeted exercises and progress tracking.
• I chaired the chess section of Crédit Coopératif’s sports association (ASCC) and the EPISEN chess club: running the clubs, organising activities and bringing members together. I handed both presidencies over after graduating. Competitive chess player, notably during my preparatory years in Cameroon.