Greetings ✨, I'm Salim MAJIDE

🙋‍♂️ About Me

Data Engineer & MLOps Enthusiast

Passionate about building scalable, reliable, and production-ready data systems, I specialize in designing end-to-end data platforms combining Data Engineering, Machine Learning, and MLOps.

I have worked on advanced projects such as a Scoring & Fraud Detection Platform (MLOps) integrating real-time streaming, ML models, and compliance automation (KYC/AML), as well as end-to-end ELT pipelines and Big Data analytics systems.

💡 My core expertise includes:

Real-time data pipelines (Apache Kafka, Spark, Airflow)
MLOps & model lifecycle (MLflow, Docker, Kubernetes)
Data quality & governance (Soda Core, monitoring, validation)
Scalable architectures (batch & streaming)

🔭 Currently Working On

Scoring & Fraud Detection Platform (MLOps)
Real-time pipelines with Kafka + Spark Streaming
Advanced monitoring & observability (Grafana, Prometheus)

🌱 Learning

Advanced MLOps (MLflow, Kubeflow, Feature Store)
Cloud Data Engineering (GCP / BigQuery optimization)
LLM integration in data platforms

👯 Collaborating On

Data Engineering & MLOps projects
Open-source data platforms & analytics tools

🤔 Seeking Help With

Advanced orchestration with Apache Airflow
Distributed system design & optimization

💬 Ask Me About

Data pipelines (Batch & Streaming)
MLOps & ML lifecycle
Data modeling & architecture
Fraud detection & scoring systems

📂 Projects Portfolio

🚀 Here are some of my database projects:

Project	Description	Tech Stack
Plateforme de Scoring Automatisée avec MLOps (All-in-One)	Plateforme intelligente, scalable et traçable permettant l'évaluation du risque de crédit (scoring prospects) + l'intégration d’un chatbot intelligent
Football Data Engineering ( Databricks + Power BI)	Pipeline complet pour analyser les performances footballistiques
YouTube ELT Pipeline (Airflow + Data Quality)	Pipeline ELT automatisé pour analyser les données YouTube
Sentiment Analysis API	API REST et page web pour analyse de sentiments via Hugging Face
Big Data Apps Deployment in Kubernetes	Automatisation du déploiement d’applications Big Data dans Kubernetes
Kafka Pipeline in Big Data Environment	Création et déploiement d’un pipeline Kafka pour ingestion et traitement en temps réel
REST API + MongoDB (Django ORM + pymongo)	API REST avec stockage de données MongoDB et WebSockets
Bank-Account-Application	Application bancaire Java avec Spring Boot, AngularJS et JWT
Web Chat Application (Java EE)	Chat en ligne avec MVC, JSP et Spring Data JPA

🛠 Capabilities

Database Design & Optimization : Relational & NoSQL schema design, query tuning, indexing, normalization
ETL & Data Processing : Apache Spark, Kafka, Airflow, data cleaning, transformation, pipeline orchestration
Machine Learning & AI : NLP models, clustering, classification, predictive analytics (Random Forest, Logistic Regression)
Automation & DevOps for Data : Kubernetes, Docker, CI/CD for scalable deployments
Data Visualization & BI : Tableau, Power BI, dashboard creation, KPI monitoring