Zaradacht Taifour

- Led cost optimization initiatives, reducing GCP spend by 30% (from $10,500/month to $6,500/month) - Managed GCP infrastructure with deployments on Google Kubernetes Engine (GKE), optimizing resource usage and performance - Owned the full development lifecycle: new features, incident resolution, and production reliability - Developed multiple data connectors (API, FTP) from Snowflake to BigQuery and BigQuery to FTP - Built and maintained Lacoste’s data platform on GCP, covering both functional requirements and infrastructure build/run - Worked closely with business teams to understand needs and deliver scalable data pipelines and analytics models using dbt - Designed and implemented new connectors and ingestion logic to support evolving business needs

# Churn Scoring Project – Wafa Assurance - Designed and deployed a POC for churn scoring combining a Data Platform on Azure and a Machine Learning model. - Designed and implemented a secure data migration solution to Microsoft Azure, including an anonymization layer and synchronization between on-premises and cloud environments. - Built a three-layer (Bronze, Silver, Gold) architecture following best practices for data management in Azure Data Lake. - Integrated and ingested data from Snowflake via Airbyte into Azure Data Lake Storage Gen2. - Developed ETL/ELT processes using Databricks, Python, and SQL, while managing data security with Azure Key Vault.

- Built and maintained ingestion pipelines from Cloud Storage to BigQuery - Industrialized new data flows via SFTP, Cloud Storage, and Kubernetes - Developed and maintained dbt transformation pipelines - Centralized data from multiple sources (Snowflake, GA4, Cloud Storage) into BigQuery - Managed and monitored infrastructure on Kubernetes - Created and maintained Ads connectors to push marketing audiences to Facebook, Criteo, and Google Ads - Ingested data from Snowflake using Airbyte

- Developed and maintained marketing data ingestion pipelines to analyze ROI on ad campaigns - Built a reliable and secure data ingestion pipeline from Google Ads Data Hub to BigQuery - Implemented data backup mechanisms within the code to ensure data availability and resilience - Deployed the application using Artifact Registry and Google Kubernetes Engine (GKE), optimizing scalability, availability, and cost-efficiency

- Built and maintained data pipelines for 8 bank subsidiaries on Cloudera Data Platform (CDP) - Worked in an agile team with senior/junior engineers and participated in sprint ceremonies and client workshops - Transformed and exposed data using Spark Scala, Hive, and HBase across marketing and finance domains - Developed internal fraud detection pipelines using Spark Scala - Automated Oozie workflow generation with Jinja + Python - Streamed and processed data from Kafka, exposing it via Hive/HBase - Improved and challenged existing Spark Scala codebase with strong testing and review processes - Automated manual tasks using Python and Bash scripts - Secured data access using Apache Ranger and handled deployment from dev to prod

- Comprehensive training in data architecture, ETL pipelines, big data processing, cloud technologies, and machine learning. - The program combined theoretical foundations with hands-on projects using Python, SQL, Spark. - Completed a full-time research internship focused on smart energy forecasting using advanced metering infrastructure.

- Gathered data using real tools - Assisted in cleaning the data, and developing machine learning models to classify the machines vibrations into 5 classes, from the least severe vibrations to severe using KNN