Houssameddine Abdel-Laoui

I assisted several clients in various sectors (luxury, e-commerce, media) in the design and implementation of modern and scalable data platforms on AWS and Azure. My expertise covers the entire data lifecycle, from ingestion to visualization, including transformation and modeling.

My key achievements and skills:

✱ I developed ingestion pipelines on AWS Glue and AWS Step Functions (or Databricks and Azure Data Factory), capturing near real-time data from various sources (APIs, files, databases). I implemented initial quality controls, managed anomalies, and archived corrupted data to ensure a reliable Bronze layer.

✱ I optimized pipelines to clean, transform, and enrich data, using PySpark and SQL Spark to automatically validate schemas, eliminate duplicates, and verify business rules. This allowed me to prepare high-quality data for the Silver layer.

✱ I used DBT (Data Build Tool) to orchestrate and industrialize data transformation, applying best practices for versioning and documentation.

✱ I designed data models (star, snowflake) to structure key performance indicators (KPIs) and optimize analytical query performance using AWS Glue Data Catalog and Amazon Athena (or Delta Lake).

✱ I developed ready-to-use datamarts, connected to Amazon QuickSight and Power BI with dashboards, anomaly alerts, and complete documentation (data dictionary) for business users.

✱ I orchestrated workflows with Apache Airflow (MWAA on AWS, or Azure Data Factory for simple cases), integrating continuous monitoring and quality alerts (schema validation, drift detection). I also performed regular audits to ensure the reliability and stability of production pipelines.

As a data engineer at Amazon, I designed and optimized large-scale distributed data pipelines. I proposed and implemented Databricks as a central solution, complementing AWS services (Redshift, S3, Glue), to improve the efficiency of logistics and analytical processes.

Key achievements:

✱ Design and maintenance of pipelines on Databricks and AWS Glue with Apache Spark for the ingestion and processing of multi-source data (databases, APIs, files).

✱ Data transformation (cleaning, enrichment) with schema validation, anomaly management, and application of business rules.

✱ Modeling of aggregated data on Redshift to calculate logistics KPIs and optimize analytical queries.

✱ Development of analytical tools on Databricks, integrated with Power BI and Tableau, for real-time monitoring of distribution center performance.

✱ Collaboration with technical and business teams to document pipelines and train end-users.

Impact:

✱ Reduction in processing times and improvement in data quality.

✱ Optimization of logistics operations through actionable KPIs and real-time insights.