Fadwa EL AMRAOUI

NiFiPulse – Solution On-Prem Monitoring & Alerting for Apache NiFi Developed in collaboration with DXC CDG team to improve operational visibility and system reliability of Apache NiFi clusters. Implemented near real-time monitoring for CPU, RAM, disk usage, queue depth, processor status, and flow performance metrics. Integrated Prometheus for metrics collection, Grafana for time-series visualization dashboards, and Power BI for interactive dashboards and reporting. Built ETL pipelines in Python with structured storage in PostgreSQL (Star Schema). Designed a fully Dockerized, on-prem ready architecture for production deployment. The project highlights the importance of team collaboration in delivering robust, production-grade solutions in data engineering and observability. Technologies & Tools: Apache NiFi, NiFi Registry, Prometheus, Grafana, PostgreSQL, Python, Docker

- Built and automated ETL/ELT data pipelines using Pandas, PySpark, Airflow, and DBT. - Designed data models following Star and Snowflake schemas for efficient analytics. - Developed data visualization dashboards with Power BI, Matplotlib, and Seaborn to present insights clearly. - Worked with cloud and distributed systems such as Azure, Databricks, Apache Spark, Kafka, and Terraform. - Strengthened programming and scripting skills in Python, SQL, and Bash. - Collaborated in agile team environments, simulating real-world data engineering workflows.

Conçu et développé un système prédictif de Machine Learning pour estimer les délais de traitement des tickets Help Desk. Création d’un pipeline ML modulaire avec preprocessing avancé (ColumnTransformer), modélisation (XGBRegressor) et intégration NLP (TF-IDF) pour analyser les descriptions des tickets. Déploiement d’une interface web avec API permettant à l’équipe support d’accéder directement aux prédictions. Résultats : amélioration de la précision des estimations et optimisation de l’efficacité opérationnelle. Technologies: Python, Scikit-learn, XGBoost, Flask/FastAPI, PostgreSQL.