Hamza E.

➔ Led the development of a real-time customer engagement analytics platform tracking purchase patterns and consumer preferences ➔ Designed and implemented data pipelines processing customer journey data and product interaction metrics ➔ Created comprehensive dashboards for marketing performance and consumer behavior monitoring across 20+ market segments ➔ Implemented data quality controls ensuring 99.9% accuracy in customer insights reporting Tools & technologies : GCP, Python, pySpark, Tableau, Terraform, Git

➔ Architected end-to-end customer analytics platform processing data from 30+ countries, including tracking user website behaviors and interactions. ➔ Developed data pipelines for product recommendation engine to improve sales. ➔ Created automated reporting system for discount optimization and inventory management. ➔ Updated existing notebooks to ensure KPIs remained functional during migration from GA3 to GA4. ➔ Designed comprehensive Power BI dashboards for marketing performance monitoring. ➔ Successfully coordinated Cloud migration, reducing processing time by 60%. Tools & technologies : Azure, Databricks, Python, Spark, Power BI

- Identification of the data necessary for the company's problems and implementation of pipelines ensuring the availability of this data. (Python, Airflow, AWS S3, MySQL.) - Collaborate with engineers, finance team, sales team and product managers to understand and translate needs into data. (Metabase, Snowflake.) - Develop data expertise and implement data controls to ensure compliance, data quality, privacy, security, and operations for assigned property domains. (dbt, SQL, pandas, Docker.)

- Creation of an API to retrieve and collect merchandise data from customs databases. (Airflow, Python.) - Establishment of a local database, backup and maintenance process. (PostgreSQL, GCP.) - Translation of the needs of professionals in statistical analysis of the data collected. - Implementation of decision support and market analysis dashboards (Tableau.)

-Automation of the process of extracting data from prescriptions sent by doctors and their implementation in databases. -Identification in exhaled air, volatile organic compounds that would identify patients with lung cancer. (Python: pandas, numpy, scikit-learn.) -Creating, testing and optimizing of the machine learning model allowing the prediction of potential future patients to be affected by lung cancer. (TensorFlow and Keras.)