About

Surabhi Sharma is a highly skilled Data Engineer with over 5 years of experience in the field. With a background in Computer Science, Surabhi has a Bachelor of Technology degree from GLA University. She has worked with some of the biggest names in the industry, including Optum, PwC, and Infosys. Currently, Surabhi is working as a Data Engineer at Optum, where she is responsible for developing advanced statistical models and machine learning algorithms to derive data-driven insights for multiple divisions. She is comfortable working with a range of technologies, including Azure, AWS, and GCP. Her skill set includes Data Engineering, AWS, CI/CD, Spark, Python, SQL, Terraform, ML, AWS Glue, Amazon Athena, Big Data, Kubernetes, Docker, NoSQL, Azure, GCP, Kafka, and AI. In her previous roles, Surabhi has worked on creating data lakes using Kafka and Spark for real-time streaming at PwC. At Infosys, she focused on developing machine learning processes, performing statistical analysis to resolve data set problems, and enhancing the accuracy of their AI software's predictive automation capabilities. Surabhi is a skilled data developer with rich experience in algorithms, solution development, strategy formation, and model building using Python. She has a deep understanding of big data and Spark, Python, PySpark, Lambda, SQL, and NoSQL databases. She has also worked with Terraform and Docker/Kubernetes and has knowledge of AWS/Public Cloud scripting and automation for continuous deployment and integration (CI/CD). She has used AWS Glue, Glue Data Catalogue, AWS Athena, Redshift, and EMR for development of user business problem-solving and has knowledge of monitoring and alerting. Overall, Surabhi Sharma is a highly skilled Data Engineer with a wealth of experience in the industry. She is passionate about using data to drive insights and is comfortable working with a range of technologies to achieve this goal.