Mohamed EL IBRAHIMI

- Developing and maintaining data pipelines to efficiently collect, process, and store data from various sources - Developed and maintained robust ETL pipelines using tools like Airflow , Redshift , s3 to efficiently process and store large volumes of data, improving system performance and reliability. - Designed and implemented data models and warehouse structures in Redshift and BigQuery to meet evolving business requirements and optimize data storage. - Automated data processing workflows with Python and SQL to reduce query costs, enhance scalability, and ensure timely data availability for analytics. - Built API services using Python and integrated with REST to streamline data access, reduce direct database queries, and optimize infrastructure efficiency. - Integrated DataHub for data lineage and cataloging to improve transparency, traceability, and access across teams. - Led the migration and creation of dashboards form Tableau to in Looker Studio and Superset, improving accessibility and delivering business-critical insights with cost-effective tools. - Developed automated solutions with Python to handle marketing and sales data requests, improving data segmentation and campaign targeting efficiency. - Built and deployed machine learning models to improve content moderation, And leveraging Open AI to enhance user experience and reduce spam. - Implemented a data democratization service using dbt and MetricFlow to empower teams with trusted metrics and drive data-driven decision-making.