Data Analytics Engineering graduate with a Master's degree from George Mason University (GPA: 3.85/4.0), specializing in building end-to-end data pipelines, machine learning models, decision-ready dashboards, and LLM-driven analytics systems. I bring hands-on expertise across the complete data lifecycle, from ETL design and cloud infrastructure to advanced machine learning, deep learning, and Large Language Model applications. My technical foundation spans Python-driven data engineering, SQL, PySpark, Big Data, statistical modeling, data processing frameworks, modern BI tools, and AI-powered solutions, with practical experience in architecting cloud-native data systems on AWS (S3, Glue, EMR, Aurora, Redshift, Lambda) as I hold AWS Certified Data Engineer – Associate & Cloud Practitioner certifications, and designing operational data stores, implementing production-grade ETL workflows using SnapLogic and Databricks, and have demonstrated expertise in prompt engineering, database management, feature engineering, model evaluation, and data-driven storytelling.

I am actively seeking challenging data engineering and analytics roles where I can leverage my technical expertise in cloud data infrastructure, machine learning, and AI to build scalable, production-ready systems that drive measurable business impact.