Sai Teja
About Sai Teja
Sai Teja is a Senior Data Engineer at General Mills with extensive experience in big data technologies and cloud solutions, having worked in both India and the United States.
Company
Sai Teja is currently employed at General Mills, a global manufacturer and marketer of branded consumer foods. He serves as a Senior Data Engineer, leveraging his expertise in data engineering and cloud solutions to contribute to the company's data-driven initiatives.
Title
Sai Teja holds the position of Senior Data Engineer. In this role, he is responsible for designing, developing, and optimizing data pipelines, ensuring robust data architectures to support business analytics and decision-making.
Professional Background
Sai Teja has gained substantial work experience in both the United States and India, transitioning internationally in his career. He began at Konylabs IT Service Pvt Ltd as a Big Data Engineer for two years in Bengaluru, Karnataka, India, followed by a similar role at Value Momentum Software Services Private Limited in Hyderabad, Telangana, India. He then moved to the United States, where he worked as an AWS Data Engineer at Ameriprise Financial Services, LLC, and later as a Senior Data Engineer at Toyota North America in Dallas, Texas.
Expertise in AWS and Azure
Specializing in Azure data engineering and cloud solutions, Sai Teja brings extensive experience in utilizing big data technologies. His proficiency covers both AWS and Azure cloud platforms. This dual competency enables him to handle diverse projects across various industries, providing versatile cloud-based data engineering solutions.
Industry Experience
Sai Teja has worked on numerous data engineering projects across multiple industries. His roles have spanned automotive at Toyota North America, financial services at Ameriprise Financial Services, LLC, and software services at Value Momentum Software Services Private Limited and Konylabs IT Service Pvt Ltd. This broad industry experience has equipped him with a diverse set of skills applicable to different data contexts.