Aspiring Data Engineer with a passion for building scalable pipelines, optimizing cloud data systems, and applying AI to solve real-world problems across industries like healthcare, finance, and sports. With hands-on experience at companies like LTI Mindtree, Cognizant, and UMBC, I’ve architected end-to-end solutions that improve efficiency, reduce cost, and drive smarter decisions. At UMBC, I’ve led initiatives both as a mentor and an AI researcher—empowering students to build robust ETL pipelines while fine-tuning transformer models like GPT-2 and BART for more coherent generative outputs. Prior to that, I worked on distributed data systems and cloud migration projects, boosting ETL throughput and cutting down query times by 5x. Career Highlights: ★ Increased ETL pipeline speed by 30% at LTI Mindtree using Spark + AWS optimizations ★ Delivered a multilingual medical summarizer (15+ languages) with 95% accuracy for 200+ professionals ★ Automated financial data ingestion with PySpark, improving risk analysis performance by 40% ★ Mentored 20+ students in Python, Airflow, and AWS, improving project delivery rates by 40% Beyond work, I love experimenting with open-source AI models, watching NBA games, and sketching in my free time. I'm also deeply interested in building products that make a positive difference in people's lives—especially in the healthcare and education sectors. Certified in AWS, and skilled in Python, SQL, Spark, Snowflake, and Airflow, I’m actively seeking full-time roles in Data Engineering or Applied AI where I can contribute, grow, and innovate. 📬 Feel free to connect if you’re hiring, collaborating, or just love talking data and AI : satheeshm1202@gmail.com