Malek Idri

Key missions: Design and optimization of scalable data infrastructures, guaranteeing the quality, governance and availability of data for data-driven decisions. Achievements & Skills ✅ Data pipeline architecture Design and maintenance of high-performance ETL/ELT pipelines (Python, DBT, Airflow) to process various data volumes. Development of API/webhook connectors to synchronize heterogeneous systems, improving data integration. ✅ Data warehouse management & governance Optimization of the Snowflake warehouse (modeling, costs, performance) and implementation of governance rules (metadata, lineage, quality). Automation of data monitoring through proactive alerts. ✅ DataOps & Industrialization Deployment of containerized solutions (Docker) for reproducible and scalable environments. Exhaustive documentation of pipelines and best practices, accelerating the onboarding of new collaborators. ✅ Cross-functional collaboration Close work with Data Business teams to operationalize ML models (e.g. financial return optimization). Support to analysts via the provision of reliable and documented datasets. Technical stack: Python, SQL, Apache Airflow, DBT, Snowflake, Docker, Azure, AWS. Actively monitoring industry innovations (Data Mesh, streaming, etc.) to propose modern solutions.

Objectives: Creation and maintenance of data pipelines in the Orange France Data Factory. -Creation of workbench with SSIS and T-SQL from the DATA lake "Hube Data" -Creation of jobs and scheduling -Exploitation/Visualization Power BI/SSAS -Maintenance and improvement of the data delivery chain.

Data project, carried out as a team as part of the Data Engineer training. Objectives: "Predict whether a customer is likely to leave the company" - Exploration and selection of data under Python with Numpy and Pandas - Data Cleaning, Oversampling and Visualization with sklearn - Training and evaluation of Machine Learning models - Development of Python APIs with FastAPI - Automation of tests with PyTest - Creation of Docker containers - Orchestration via Kubernetes on several pods under AWS - Creation of a Neo4J database and FlaskAPI implementation