Zeeshan Aziz

Designed and implemented scalable data pipelines using Azure Databricks, PySpark, ADF, Azure Data Lake, and Microsoft Fabric to process over 30TB of multi-source data for AI/ML models and analytics. Built end-to-end ETL workflows leveraging Dataflows Gen2 and Fabric Lakehouses, ensuring efficient ingestion, transformation, and quality validation of real-time data.

Developed data ingestion pipelines using Python, SQL, Databricks, Azure Data Factory, and Microsoft Fabric to load over 50TB of financial product data into Oracle and Fabric Lakehouse environments. Used Apache Spark on Azure Databricks with Delta Lake tables to process, standardize, and manage large datasets for downstream analytics and systems.

Engineered end-to-end data pipelines and data warehousing solutions leveraging Python, SQL, C#, and Apache Spark within the Azure cloud ecosystem, utilizing services such as Azure Functions, Azure SQL Database, Postgres DB, Databricks, Synapse Analytics, Data Lake Storage, and Data Factory.

Developed a .NET/C# library for managing application state, designed for use in data-driven front-end applications, including comprehensive documentation (JSDoc) and mirroring the development and deployment of data tools and APIs within a data engineering ecosystem like Azure. Developed and maintained a robust data pipeline using Python, SQL, Apache Spark, and cloud-based services like Azure Datab

Contributed to the development and optimization of data pipelines and workflows, leveraging Azure Data Services for efficient ETL/ELT processes, implementing advanced data modeling techniques, and ensuring robust data governance and security. Developed automation scripts using Python, SQL, C#, and integrated Big Data technologies to enhance scalability and performance.

Grade: 3.7 CGPA

Bachelor of Science in Computer Science from Government College University, Faisalabad, with a CGPA of 3.7.