I am a Data Engineer with extensive experience in building data pipelines and deploying analytics solutions using platforms such as Databricks, Snowflake, and Tableau. I specialize in gaining actionable client insights and collaborating effectively with cross-functional teams to deliver impactful results. My technical skills include SQL, Python, AWS, and adapting quickly to emerging low-code technologies.
Throughout my career, I have contributed to various projects that improved data processing efficiency and accuracy. At LTIMindtree, I designed scalable AWS-based data architectures and optimized ETL processes, significantly reducing query runtimes and enhancing data warehouse performance. I also integrated machine learning models using AWS SageMaker, which decreased model training times and improved anomaly detection accuracy.
My internship experiences have strengthened my expertise in cloud infrastructure, data visualization, and data science techniques. I have worked with Azure Data Factory, Power BI, and applied statistical and machine learning methods to solve real-world problems, such as improving educational outcomes and product satisfaction.
I hold a Master of Science in Data Analytics Engineering from George Mason University and a Bachelor of Engineering in Electronics and Communication Engineering. I am also an AWS Certified Data Engineer – Associate, which validates my proficiency in cloud-based data engineering solutions.
I am passionate about leveraging data to drive strategic decision-making and continuously enhancing my skills in emerging technologies such as generative AI, deep learning, and large language models. I am open to relocation and eager to contribute to innovative data engineering projects that create meaningful impact.
