Hanan Ahmad

I’m a data engineer with hands-on experience building scalable batch and streaming data pipelines and cloud-based analytics solutions using Azure and GCP. I focus on turning raw data into reliable, structured datasets through ETL/ELT, strong validation, and thoughtful data modeling.

In my current role as an Associate Data Engineer, I designed and maintained pipelines that support analytics use cases, processing large-scale datasets on Azure and GCP. I developed PySpark jobs in Databricks to transform raw data into structured datasets, implementing data validation checks and improving data quality, while optimizing ETL workflows to enhance performance and reduce processing time.

Through projects, I’ve built end-to-end lakehouse and analytics platforms—using Azure Data Lake Storage Gen2, Delta Lake, and Medallion Architecture—to deliver scalable transformations with Azure Data Factory and Databricks (PySpark). I also created GCP ingestion and modeling workflows with Document AI, Cloud Storage, and BigQuery, and built KPI dashboards with dbt and role-based access control for secure multi-tenant access.

I’m energized by problems that blend data engineering and applied ML. My final-year project combined Flutter, Kotlin Native, and TensorFlow to deliver a real-time Arabic sign language interpreter, where I trained a model reaching up to 90% accuracy in sign recognition.