Muhammad Zaid

Spearheaded the successful migration of enterprise data from on-premises Oracle SQL database to Azure Data Lake Storage Gen2, establishing a secure and scalable foundation for cloud-based analytics.

Engineered and optimized high-performance data pipelines with Apache Spark, achieving a 40% reduction in data latency across large distributed datasets.

Developed and fine-tuned over 30 complex SQL queries and stored procedures, boosting ETL efficiency and reporting performance by 35%.

Implemented comprehensive data validation, schema checks, and unit testing frameworks, ensuring end-to-end pipeline reliability and enhancing data quality assurance.

Integrated diverse Azure services, including Data Factory, Data Lake Storage Gen2, and Databricks, to establish secure, code-driven data movement and transformation workflows.

Facilitated critical Proof-of-Concepts (POCs) for Azure data migration, achieving 100% accuracy and efficiency in data ingestion, transformation, and storage processes.

Drove 100% consistency across a 4-member team during POCs focused on developing robust data workflows within Azure Data Factory.

Implemented advanced PySpark optimizations, including partitioning, broadcast joins, and caching, significantly reducing job runtimes and enhancing cluster utilization.

Executed comprehensive performance tuning on Spark jobs and SQL queries, resulting in a 30-40% improvement in data processing times during critical POC validation cycles.