Mehdi KENANA

• Architected and optimized dbt models within a Medallion architecture on Microsoft Fabric, significantly enhancing scalability, maintainability, and performance across Bronze, Silver, and Gold data layers. • Developed a robust data quality testing framework leveraging dbt generic and unit tests, improving data reliability across analytics pipelines. • Collaborated on the migration from Azure Data Factory to Apache Airflow, contributing to standardized DAG designs, reducing pipeline execution time and operational overhead. • Implemented end-to-end CI/CD workflows in Azure DevOps, automating testing and deployment in production.

Fintech offering loans to E-Commerce and SaaS entrepreneurs in France and Germany • Data Engineering: - Built data pipelines from various sources to datamarts in BigQuery using dbt - Reduced 60% of business scoring leadtime by automating MRR metrics computation from various connectors using SQL - Implemented Airflow DAGs fetching data from APIs (Pennylane, Quickbooks) • Data Science: - Improved revenue forecasting by 20% on average using a custom Prophet model - Implemented an NLP model to categorise bank transactions according to their label • Product Analytics: - Created several Data Studio dashboards tracking adoption of internal tools by various stakeholders (Sales, Operations, Risk, Finance) - Helped PMs define their roadmaps by identifying conversion funnel bottlenecks

Leveraging my data and analytics skills, I advised companies, investment funds, regulatory authorities and other government entities on various strategic and network economics issues: - Sectoral regulation - Antitrust and Competition - Litigation - Corporate Strategy and Economic Policies - Cost modelling Examples of issues covered: European electricity wholesale market, Roaming costs, MVNO regulation, State aids, Interconnections and wholesale regulated prices, fibre investment schemes, cash-flow analysis, Universal Service Obligations, etc.

• Created Tableau dashboards monitoring risk metrics • Assessed risk metrics (VAR, SVAR, stress-tests) on a daily basis using SQL, Python and VBA • Supported algorithmic trading desk with various ad-hoc tasks