Mrad Ilyes

(pandas, requests,

BeautifulSoup, playwright, Selenium), Airflow, Delta Lake.

• • Designed and implemented intelligent web scraping solutions to collect structured and unstructured data from multiple

financial and media sources.

• • Developed and automated scraping pipelines to ensure reliable and regular data extractions.

• • Stored and managed scraped data using Delta tables on S3 to ensure data versioning, consistency, and e!cient

incremental processing.

• • Deployed and managed AWS Lambda jobs using CloudFormation templates.

• • Orchestrated data workflows and pipelines with Apache Airflow to improve reliability and monitoring.

• • Collaborated with the business and technical team to ensure strict adherence to security and compliance requirements.

Mission with France. ( Sevices: GLUE, LAMBDA, S3, Athena, Step Functions), Snowflake,

Delta Lake.

• • Spearheaded the automation of end-to-end data pipelines using AWS Glue, Databricks (Spark + Delta Lake), and

Snowflake, significantly improving data processing e!ciency, reliability, and time-to-insight.

• • Collaborated closely with Bpifrance teams to design and implement scalable data solutions on Databricks Lakehouse,

addressing diverse analytical and business requirements.

• • Implemented Delta Lake tables on S3 to ensure data consistency, versioning, and performant incremental processing.

• • Orchestrated workflows using AWS Step Functions to trigger and monitor Databricks jobs (via Lambda), enhancing pipeline robustness, observability, and operational control.

• • Supervised and mentored interns, providing guidance on project methodologies and facilitating their learning to ensure

their e"ective contribution to team objectives.

• • Automated data pipelines utilizing AWS Step Functions and Apache Airflow to orchestrate SageMaker and Glue jobs,

ensuring seamless data processing and robust workflow integration.

• • Designed a robust data quality check framework, complete with a real-time and batch monitoring dashboard to ensure

data integrity.

• • Optimized ETL processes to enhance data flow e!ciency, reducing processing time by implementing parallel processing

and leveraging cloud-native services.

• • Managed deployment of applications using Kubernetes, ensuring scalability and high availability.

• • Provisioned and managed cloud infrastructure using Terraform, optimizing resource usage and maintaining infrastructure as code (IaC) practices.

• • Scaled node groups and orchestrated machine learning tasks scheduling with Airflow, optimizing computa tional resources and workflow e!ciency.

• • Developed scalable machine learning models for predictive analytics, enabling accurate forecasting and

data-driven decision-making.

• • Built a Reinforcement Learning–based risk management system to optimize dynamic decision-making under

uncertainty.