Muhammad Zaid

Led enterprise data migration and optimized scalable data pipelines, enhancing analytics and reporting for cloud-based solutions.

• 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 data migration POCs to Azure and implemented PySpark optimizations to enhance data processing efficiency and cluster utilization.

• 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.