Andrew Minneci
About Andrew Minneci
Andrew Minneci is a Data Engineer with a background in Computer Science and Statistics from the University of Wisconsin-Madison. He has worked at Placon Corporation and currently works at Remind, focusing on innovative statistical methods and efficient data storage solutions.
Work at Remind
Andrew Minneci has been employed at Remind as a Data Engineer since 2021. In this role, he focuses on utilizing innovative statistical methods and efficient data storage solutions to enhance data management processes. His responsibilities include managing and storing data effectively through the use of both relational databases (RDBMS) and NoSQL databases. Andrew's expertise in data engineering allows him to contribute to projects that require adaptability to changing data patterns.
Previous Experience at Placon Corporation
Before joining Remind, Andrew Minneci worked at Placon Corporation as a Data Engineer from 2019 to 2021. During his tenure at Placon, he developed skills in data management and engineering, which laid the foundation for his current role. His experience at Placon contributed to his understanding of data systems and the implementation of effective data solutions.
Education and Expertise
Andrew Minneci earned a Bachelor’s Degree in Computer Science and Statistics from the University of Wisconsin-Madison, where he studied from 2011 to 2015. His educational background provides him with a strong foundation in both statistical analysis and computer science principles. This combination of knowledge supports his specialization in data engineering, particularly in the application of machine learning and trend analysis in data projects.
Technical Skills in Data Engineering
Andrew Minneci specializes in making data 'dance' through innovative statistical methods. He effectively utilizes both relational databases (RDBMS) and NoSQL databases to manage and store data. His technical skills include incorporating machine learning and trend analysis into his data engineering projects, allowing him to adapt to evolving data patterns and enhance data-driven decision-making.