Thomas Schneider
About Thomas Schneider
Thomas Schneider is a Senior Data Engineer specializing in Digital Health at Kry in Stockholm, Sweden, where he has worked since 2021. He has a strong background in data analytics and engineering, with previous roles at Medbelle and NORD/LB, and holds a Master of Science in Business Economics from Humboldt-Universität zu Berlin.
Work at Kry
Thomas Schneider has been employed at Kry as a Senior Data Engineer in the Digital Health sector since 2021. He is based in Stockholm, Stockholms Län, Schweden. In his role, he is part of a six-person data infrastructure team within a larger analytics department of thirty individuals. His responsibilities include migrating the company's data infrastructure from Redshift to Snowflake and utilizing a custom pipeline system for Extract, Load, and Transform (ELT) processes.
Education and Expertise
Thomas Schneider holds a Master of Science (M.Sc.) in Business Economics from Humboldt-Universität zu Berlin. He also studied European Economic Studies at Otto-Friedrich-Universität Bamberg, where he earned a Bachelor of Science (B.Sc.). Additionally, he studied Applied Economics at Budapest University of Technology and Economics, achieving a Bachelor of Arts (B.A.). His educational background supports his expertise in data engineering and analytics.
Professional Background
Before joining Kry, Thomas Schneider worked at Medbelle as a Data & Analytics Manager in Digital Health from 2019 to 2021. He also held positions as a Business Intelligence Analyst at Homebell and a Business Intelligence Intern at NORD/LB. His early career included an Operations Intern role at Panalpina and an Analytics working student position at KIB-Gruppe. These experiences contributed to his development in data analytics and engineering.
Technical Skills and Responsibilities
In his current role, Thomas Schneider utilizes a tech stack that includes AWS services such as Redshift, S3, EC2, ECS, and Glue. He is responsible for optimizing queries, designing data warehouse architecture, and productionalizing machine learning scripts. His role also encompasses DevOps, Continuous Integration/Continuous Deployment (CI/CD), and Quality Assurance (QA) for data pipeline-related services, showcasing his technical proficiency in data infrastructure.