Mohamed El Guendouz

Multi-layer Dataform for providing reliable and usable data (bronze → silver → gold).

Orchestration and automation of data pipelines via Cloud Composer (Airflow).

Development of Talend jobs for extraction from on-premises Teradata, migration to GCP, and transformation in Dataform.

Co-construction of a Power BI cockpit for managing store operations: monitoring revenue, margins, stock, and year-over-year comparisons.

Creation of shared Data Products and implementation of the Vendor Gold Model, a cross-functional analytical model used by several squads (Marketing, Retail, Product Offer, etc.).

Continuous improvement of data flow performance and reliability: monitoring, execution time optimization, and quality control.

The objective of this mission was to optimize France Télévision's marketing campaigns by implementing a data processing project, while offering a flexible and customized solution to the company's specific needs.

To achieve this, a cross-functional team was formed and chose a robust and reliable architecture, which included Terraform for infrastructure, Cloud Composer for orchestration, Spark jobs for processing, and Deltalake files for storage.

The team used the Scrum methodology to frame Use Cases and adapt to evolving processes and technologies. As a Data Engineer, I contributed to improving the organization's efficiency and performance by implementing a solid and reliable architecture, reducing costs, and optimizing resources.

My work contributed to better decision-making and data management.

As part of a short and well-defined mission, I worked as a Data Engineer to help a team finalize their projects before the end of the year. My objective was to implement an efficient solution for ingesting data stored on an on-premise Oracle server into a GCP cloud environment for processing.

We started by developing a script to ingest FULL data into a GCP storage bucket, optimized to ensure complete and reliable data ingestion while managing errors. Then, we used Terraform to create the necessary resources for data processing on GCP, including virtual machine instances, security rules, and network connections to enable communication between different components of the architecture.

We also used Dataform to create the necessary SQLx queries for data transformation and setting up processing pipelines, which simplified the management of SQLx queries by offering a high-level abstraction for defining tables and views.

After setting up the architecture, we tested and validated the complete process of data ingestion from Oracle to GCP, as well as the transformation and processing of data on GCP.

This mission was a success, with the implementation of a high-performance solution for ingesting data from an on-premise Oracle server to GCP, with optimized resources and SQLx queries for data processing on GCP. It also allowed me to develop solid skills in data engineering, data architecture, cloud platform deployment, and multidisciplinary team coordination.