YOUNESS MAMMA

• ETL and data pipeline development • Apache NiFi workflow development and integration • Apache Airflow orchestration • Apache Spark data processing and transformation • Kafka-based streaming and messaging workflows • AI/LLM integration workflows (including vLLM-based services) • TensorFlow and AI integration where applicable • Data ingestion, normalization, and transformation pipelines • Analytics and reporting data preparation • Integration with backend services and AI/data infrastructure Tools: Apache NiFi, Apache Spark, Airflow, Python, Git, Docker, GitLab, Kubernetes

• Developed and maintained ETL/ELT pipelines to support data integration and analytics. • Designed and implemented cost-reducing data warehouse solutions to enable fast querying and BI reporting. • Collaborated with startup teams and agencies to define data requirements and deliver production-ready solutions.

• Developed and optimized ETL pipelines using Apache NiFi to process banking data from S3, achieving a 50% reduction in processing time. • Built orchestration pipelines, enabling LLM inferencing on client data and generating analytical reports. • Implemented horizontal scaling of Kafka to process 3M+ daily transactions, deploying all pipeline services on Kubernetes through automated CI/CD in collaboration with DevOps team.

• Built automated pipelines ingesting 1K+ weekly research papers via AWS Lambda and CloudWatch Events. • Managed and versioned data pipelines for text analysis and generation models using MLflow. • Deployed a Flask web application on Amazon EC2, enabling LLM queries on ArXiv articles.

Published as part of the 8th IEEE Congress on Cyber Science and Technology (CyberSciTech 2023), our research article "LIDarknet: Using Ensemble Learning for Network Traffic Classification" received acceptance. distinction by IEEE