Ahmed Ben debba

I owned the core data pipelines supporting Product Analytics, CRM, and compliance, and played a key role in structuring the company’s data platform. • Data Platform Reliability & Cost Efficiency – Owned ingestion and transformation pipelines with a strong focus on reliability, observability, and cost optimization. – Standardized pipeline patterns and improved engineering best practices across the Data team. Stack: Apache Airflow/Cloud Composer, SQL, Python • Compliance & Monitoring – Implemented GDPR-compliant processes for user data lifecycle (deletion, anonymization, access). – Set up monitoring and alerting to ensure daily operational health of all pipelines. Tools: Grafana, Stackdriver, Tableau. • Event Tracking Architecture – Led the end-to-end event tracking system: requirement gathering with Product/Analytics, event schema design, and coordination with frontend/backend teams. – Built scalable streaming pipelines ensuring reliable, real-time ingestion of application events. Stack: Apache Beam/Cloud Dataflow, PubSub, Bigquery, Protobuf, Java • CRM & Customer Data Flows – Designed and delivered batch & streaming pipelines powering CRM operations in Braze. – Translated marketing and lifecycle needs into robust, production-ready data architectures. Stack: Apache Airflow/Cloud Composer, Apache Beam/Cloud Dataflow, PubSub, Java, SQL, Python, Braze

• Development of ETL pipelines and dataflows on Airflow and GCP Cloud Composer • Architecture of datalakes and data warehouses (SQL and NoSQL) on the Cloud (BigQuery tables and Cloud Storage) • Building, packaging with Docker, deploying to production, and monitoring Machine Learning models. CI/CD and Infrastructure as Code Tools: Google Cloud Services (GCP), Python, SQL, Apache Airflow, Docker, Kubernetes, Terraform, GitLab

• Built distributed Spark pipelines processing millions of connection events daily, improving data throughput and reliability across the analytics platform. • Designed cloud-based analytical databases and developed production-ready APIs with Flask, enabling real-time access to campaign performance metrics. • Developed and deployed machine learning models (Scikit-learn, TensorFlow) in production, enhancing predictive capabilities for advertising attribution. • Tools: PySpark, Python, Flask, Scikit-learn, TensorFlow, SQL, Git

• 6-Month Internship in Deep Learning Design of a neural network model for automatic extraction of relevant content (article title, author, publication date, content, and comments) from web pages based on images (screenshots of these pages).

• Internship in Natural Language Processing (NLP) Design of a neural network model to translate a natural language request into a computer language representation: the model determines the SQL query from a sentence in English