Azas Mohammed

About Azas Mohammed

Results-driven Data Analytics Engineer with 6+ years of experience in designing and implementing scalable data solutions using SQL, Python, PySpark, Airflow, and Hive. Skilled in building robust ETL pipelines, managing large datasets, and leveraging cloud platforms such as Azure and AWS for advanced data processing and storage. Proven track record of optimizing workflows, reducing costs, and enabling data-driven decision-making through advanced analytics and visualization. Proficient in developing end-to-end data pipelines and driving strategic growth initiatives by transforming raw data into actionable insights.

Experience

1. Data Engineer

   T-Mobile

   Nov 2024 — Present

   Integrated Microsoft Fabric with Snowflake to design scalable data solutions, enabling seamless data ingestion, transformation, and visualization through One Lake and Power BI• Optimized Snowflake-to-Fabric data pipelines, reducing data load times by 30% and improving query performance for downstream analytics.• Created robust ETL pipelines in Azure Data Factory to integrate on-premises and cloud data, transforming it using Spark for Azure SQL Data Warehouse• Developed PySpark scripts and UDFs to transform and process large datasets using Databricks, boosting data processing speed and accuracy. • Developed Azure Data Factory pipelines to extract, transform, and load data from multiple sources, including Azure SQL and Blob Storage, resulting in 30% reduction in processing time and enhanced data accessibility. • Implemented Spark Streaming API to ingest real-time from diverse sources, storing transformed data in Azure Table and optimizing PySpark code, resulting in a 30% increase in data processing speed and system efficiency. • Automated Azure EventHub data ingestion with Azure Functions, streamlining high-volume data processing from multiple sources, improving reliability, and significantly reducing manual efforts. • Created and maintained Terraform scripts for provisioning Azure Virtual Network, Subnets, and Storage Accounts, ensuring secure data transfer across cloud environments.• Developed CI/CD pipelines in Jenkins and GitHub Actions for automating data pipeline deployment, reducing manual intervention by 50%. • Implemented monitoring and alerting using Azure Monitor and CloudWatch, reducing downtime by 35%. • Automated operational tasks with Python scripts and PowerShell, improving efficiency of data pipelines and reducing manual intervention by 50%. • Secured application configurations and credentials using Azure Key Vault and RBAC principles, ensuring compliance with enterprise security standards.

Education

• Vignan's Foundation for Science, Technology & Research

  Master of Business Administration, Finance, Operations

• Texas A&M University

  Masters in science, Business Analytics