Saad Amrani

Returned to the project as an expert to lead the full technical overhaul of the MGSMR tool and enhance the performance of the existing system (cf. initial experience at SNCF Réseau). • Review, optimization, and modernization of data processing pipelines using Airflow as the orchestrator. • Designed and implemented the architecture required for real-time data processing using Spark Structured Streaming and Kafka. • Applied best practices for industrialization, including testing, monitoring, and documentation.

Joined the Theodo Data team to support the full migration of the historical Redshift Data Warehouse to Google Cloud Platform for Doctolib. The scope covered the migration of dbt models, ingestion workflows (5,000+ tables), and historical datasets. • Migration of dbt models from Redshift to BigQuery : SQL translation, adaptation of warehouse logic, and refactoring. • Designed and implemented an automated “diff - patch” system to handle manual fix propagation across successive model versions. • Developed helper tools to streamline communication with the orchestration VM running dbt models (remote execution, logs, validation pipelines). • Migration of ingestion workflows : refactoring and porting heterogeneous Redshift ingestion DAGs into standardized Airflow DAGs targeting BigQuery. • Built custom Airflow operators to handle multiple ingestion patterns and legacy edge cases across 5,000+ tables. • Ensured functional and data consistency during the migration by validating lineage, schema evolution, and reconciliation metrics.

Assisted the data team in designing, developing, and maintaining various data pipelines to meet the diverse needs of the Cross-Brand product. • Set up the source code architecture in a Git repository. • Built a complete CI/CD pipeline on GitLab CI to deploy code to Cloud Run and Cloud Functions services. • Deployed a single-instance Airflow to orchestrate various daily processes. • Developed Cloud Run ETL applications to interact with the back office. • Built and maintained dbt models to prepare and transform datasets, integrated test logic, and orchestrated execution using custom Airflow DAGs.

• Collaborated with cross-functional teams to design and implement data flows for CRM department, facilitating personalized push notifications and emails based on customer actions and CRM use cases. • Implemented and maintained infrastructure within Leboncoin data platform on AWS, ensuring seamless orchestration of data flows using Apache Airflow. • Leveraged Spark for processing large datasets, optimizing computing resources for efficient execution of data flows. • Managed and optimized our Datalake and Data Warehouse to enhance performance and scalability of CRM data processes. Keywords: Python, Spark, Airflow, AWS, Redshift, Athena, AWS S3, K8s, Kafka

In charge of Data Analysis bloc for the MGSMR monitoring tool. • Algorithm Development using Python • Data Analysis & Calculation using Pyspark/Pandas. • Data Base Management with PostgreSQL. Keywords: Hadoop, Pyspark, Python, Hive, PostgreSQL

Working on #MoveInSaclay Smart Mobility Platform : • Computer Simulations & Tests. • Data Analysis. • Data Base Management. • AI Machine Learning. • Android Development. Keywords: AI, IoT, Data Visualization, Python, Java, SQL, Android Studio https://www.moveinsaclay.app/

Programming a National Instruments FPGA target with LabVIEW: • Designing a high-frequency acoustic signal sampling system • Data processing and management Keywords: NI, PXI, FlexRIO System, FPGA, LabVIEW.