Amine T.

• Designed and developed scalable ETL pipelines using AWS Glue and Spark Scala for large-scale data processing.
• Architected and maintained AWS infrastructure (S3, Glue, Lambda, IAM, Step Functions), ensuring reliability and cost efficiency.
• Built CI/CD pipelines and enforced engineering standards through code reviews and governance policies.
• Implemented data quality checks, lineage tracking, and access controls to uphold data integrity and compliance.
• Led a team of five data engineers, overseeing Terraform provisioning, Azure DevOps pipelines, and Spark performance optimization.
• Collaborated with data scientists to deploy ML models improving asset efficiency and reducing downtime.
• Delivered QuickSight dashboards enabling secure, real-time business insights.

• Designed and implemented a Data Mesh architecture on Databricks (AWS) and built governed data repositories in Glue and Collibra.
• Developed data ingestion and sharing pipelines in Spark/Scala and PySpark with full CI/CD automation.
• Created Databricks job automation tools using Terraform and orchestrated workflows via AWS MWAA.
• Designed data pipelines for predictive maintenance and passenger flow analytics using Kafka, Spark, and AWS Glue.
• Implemented data quality monitoring with Airflow and Great Expectations, ensuring high data reliability.
• Collaborated with data scientists to operationalize forecasting models for service optimization.

• Migrated production applications from HDP to Cloudera, creating and configuring multiple environments to ensure smooth transition.
• Developed CI/CD pipelines and Terraform jobs to provision and scale VMs across environments.
• Supported engineering teams throughout the migration phase, ensuring minimal downtime.
• Developed and deployed Spark Scala libraries, and orchestrated production jobs for high availability.
• Designed and implemented NiFi pipelines for ingesting data from external APIs.
• Built data ingestion and transformation frameworks for market and risk data pipelines using Spark and Hadoop.
• Automated data quality checks and implemented lineage tracking using Apache Atlas.
• Collaborated with quant teams to improve risk model data accuracy and reduce latency in downstream analytics.
• Optimized HDFS and Hive-based data lakes, improving performance and storage efficiency.
• Contributed to regulatory reporting automation, ensuring compliance with Basel III standards.

• Built Spark-based data pipelines and ETL workflows in Talend and Kafka for real-time regulatory and anti-fraud reporting.
• Developed an AWS-based architecture, transforming CSV data to Parquet and optimizing Hive tables for performance.
• Automated deployment workflows with Jenkins and Ansible, improving development efficiency.
• Created Oozie bundles and Spark Scala jobs to implement business rules and manage data in Cassandra.
• Indexed data with Solr to enable fast search capabilities and supported production deployment and monitoring.

Lansrod is an IT consulting company specializing in big data, business intelligence, and software engineering projects for enterprise clients.

• Designed and implemented data warehouse and BI solutions across multiple industries.

• Built ETL processes in Talend for integrating data from ERP and CRM systems.

• Developed dashboards and KPIs in Power BI and Tableau for client reporting.

• Assisted in data lake design and migration to Hadoop clusters.

• Conducted data audits to ensure consistency and improve reporting accuracy.

Used skills: Talend, Hadoop, Power BI, Tableau, SQL, ETL, Data Warehouse, Business Intelligence

AddVolt is a Portuguese clean technology startup providing electric systems for refrigerated vehicles, enabling emission-free transportation.

• Project management.
• Modeling of problems related to Data Science.
• Interpretation of Machine Learning models applied to real situations.
• Pre-processing of spatio-temporal data, definition of concepts related to the problem, selection of the model and its parameters, modification of some performance metrics, and benchmarking of different classification models.
• Creation of visualization tools for the results obtained through interactive maps and a video explaining the work performed.
• Comparison of different results and analysis of the proposed solutions.
• Recommendation of a new algorithm for the allocation of virtual machines.
• Deployment of the designed project.