Haroun B.

As a Data & AI Engineer, I designed an application to illustrate data quality monitoring and improvement capabilities. I developed multi-source pipelines using Delta Live Tables with a Medallion architecture and Change Data Capture mechanisms for real-time synchronization. I implemented declarative quality controls at each transformation layer, along with data governance via Unity Catalog. I also developed interactive data quality dashboards with Python and Streamlit, and integrated an LLM (Llama-3) capable of querying data in natural language, dynamically applying quality constraints, and automatically generating dashboards on demand. Finally, I designed a hybrid architecture combining structured data and LLM capabilities for action-oriented AI assistants, all within an Agile Squad, including technical documentation writing and preparation of delivery packages.

Within a strategic and international HR program, I led the modernization and industrialization of HR data flows to support the program's scaling. I designed and implemented real-time data pipelines with Azure Data Factory, Databricks, and Delta Lake, and developed PySpark jobs orchestrated via Databricks Workflows for data ingestion, transformation, and exposure. I implemented full CI/CD lifecycle automation with Azure DevOps (Git, YAML, automated deployments) in a multi-level environment (dev, pre-production, production). I collaborated with an international team of data architects in Europe and engineers in America to define the technical architecture, ensure security, and dimension resources. I also oversaw the design, development, testing, and maintenance of Big Data and BI components in a high-volume and high-performance context, while writing technical documentation and collaborating closely with business stakeholders to translate their needs into robust data architectures.

I contributed to migrating the Big Data ecosystem to a modernized platform aimed at improving analytical performance and data accessibility. I developed ETL flows on Apache Spark and Apache Impala to replace classic Hadoop using Python/PySpark, and managed data extraction from legacy PostgreSQL systems. I developed and automated processes with Python, Sqoop, and Spark, integrated via Jenkins and Bitbucket. I ensured system stability through Operational Maintenance (MCO), including identification, analysis, and resolution of production bugs. I worked on a complex infrastructure comprising multiple environments (dev, pre-prod, prod) with several data warehouses and data marts, while actively participating in Agile ceremonies and collaborating directly with Product Owners for the maturation of analytical needs.