Mohammad Affan

• Architected and maintained 15+ automated ETL/ELT pipelines using Azure Data Factory (ADF), Python, and
SQL; optimized trigger scheduling, parallel batch processing, and transformation logic to improve overall
pipeline efficiency by ~40%, reducing daily processing time from 6hrs to ~3.5hrs
• Engineered scalable PySpark data workflows on Azure Databricks to ingest and process 10M+ records/day
from 5+ heterogeneous source systems; delivered production-ready, model-consumable datasets for both
downstream BI reporting.
• Implemented a 3-tier data quality validation framework (Bronze → Silver → Gold lake layers) with 20+
automated assertion checks; slashed data errors by 35% and sustained 99.2% data integrity SLA — directly
improving training data reliability for 3+ ML models in production
• Partnered with data science and BI teams to co-design data contracts and SLA agreements for 10+ datasets
consumed by both Power BI dashboards (50+ stakeholders) and ML training jobs; maintained data
freshness within 15-minute SLA windows