Oussama Ben Brahem

• Cloud Infrastructure Management: Proficient in building and maintaining cloud infrastructure using Terraform, with a strong u nderstanding of cloud computing concepts and best practices.

• Data Modeling and Analysis: Skilled in developing KPIs and analyzing raw data using SQL to derive actionable insights and dri ve data-driven decision-making.

• Workflow Automation: Experienced in designing and implementing data workflows on GCP using services like Cloud Dataflow, Cloud Composer, and Cloud Scheduler to automate tasks and streamline data operations.

• Data Engineering: Solid understanding of data engineering principles and methodologies, with hands-on experience in data ingestion, transformation, and processing. Overall, my experience at L'Oréal's Data Factory Team as a Data Engineer allowed me to gain valuable hands-on experience in cloud infrastructure management, KPI development, and workflow automation on Google Cloud Platform. I successfully contributed to building robust data solutions that empowered the organization to make data-driven decisions and achieve strategic objectives.

I collaborated closely with researchers in the innovation laboratory to drive data initiatives aimed at advancing research and development efforts within the organization. My role encompassed a range of responsibilities, and I developed and applied a comprehensive skill set including:

• Data Engineering: Designed and operationalized data processing systems to collect, cleanse, and transform experimental data for analysis.

• Created and managed cloud infrastructure optimized for geospatial data handling on GCP, focusing on scalability and cost-effectiveness.

• Developed econometrics and machine learning modules for predictive analytics, proficient in statistical techniques and ML algorithms for identifying patterns in complex datasets.

I contributed to the development and maintenance of the Common Data Hub, a collaborative datalake that empowered various Engie Group entities to store, share, and leverage data for value creation through transformation, dashboarding, and machine learning tools.

• Played a key role in designing and implementing core functionalities of the Common Data Hub, focusing on data storage, sharing mechanisms, and user access controls.

• The development and optimization of data pipelines using Python and AWS services, enhancing data ingestion, transformation, and loading efficiency, while also implementing API endpoints with Flask for seamless integration with other systems.

• Provided technical support for maintaining and scaling the Common Data Hub infrastructure to accommodate the growing volume of data, totaling over 150TB.