Lhoussaine Aamer

End-to-end data engineering activities in Retail Media, focusing on building scalable and efficient data pipelines on GCP.

As a Senior Data Engineer, I serve as the technical lead, I exercise my skills to acquire, transform and make financial data available efficiently and precisely with Palantir. I support the BI team by ensuring they have access to the data required for creating informative dashboards and conducting relevant analyses. ✅ Design, develop, and implement data pipelines to collect, process, and store structured and unstructured data from various sources. ✅ Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and translate them into technical solutions. ✅ Optimize and tune data pipelines for performance, scalability, and reliability. ✅ Ensure data quality and integrity throughout the data lifecycle, implementing data validation and monitoring processes. ✅ Mentor junior developers.

As part of the SWAP project within Orange France, aimed at replacing the radio technology from ALU to Nokia, I contributed to the optimization of the Orange network post-SWAP using a data-driven approach focused on data processing and analysis. ✅ Detection of seasonal sites using KNN, Logistic Regression, SVM and Neural Network. ✅ Network performance (QoS) analysis after the integration of new features, utilizing pandas for data manipulation and matplotlib for visualizations. ✅ Automation of customer complaints and network anomaly processing using Python scripts, significantly reducing diagnostic time. ✅ Optimization of sites following the ALU to Nokia SWAP, with a focus on improving operational efficiency using data insights.

As part of the Green RAN project within the Orange Group, aimed at reducing CO2 emissions, I was responsible for collecting, processing, and analyzing energy data from radio networks across the Group’s global subsidiaries, in order to identify and deploy solutions to reduce energy consumption. ✅ Energy data analysis and processing: Extraction, cleaning, and analysis of large datasets (Excel and CSV formats) from radio equipment using Python, with libraries such as pandas and numpy. ✅ Energy performance visualization: Development of interactive reports and charts using matplotlib and seaborn, facilitating the identification of efficiency gains and informed decision-making. ✅ Proposed new energy management rules: Based on data insights, I recommended and quantified potential energy savings by improving existing solutions. ✅ Collaborate with Research/Anticipation entities with Green-related programs (SON team, R&D Ericsson)