Utility infrastructure assets in the United States continue to grow as millions of utility features were installed within the properties of state and local agencies. With this growth, the management of the utility data records is becoming a complex problem in terms of large amounts of data. On one hand, management of data for utility infrastructures is extremely valuable to state and local agencies because the timely access to utility-related information is a significant requirement for the delivery of construction and renovation projects on time and within budget. On the other hand, many challenges arise, such as difficulties in effective data storage of complex and messy datasets, data analysis, and data visualization. Utility owners face challenges in collecting utility data in standardized formats, data storage, and providing easy access to all stakeholders. Using a case study in Nevada, this paper demonstrates how tools and a strategic workflow process can be harnessed to develop an end-to-end management solution for large and complex data of a utility infrastructure. This end-to-end utility data management solution builds upon existing systems which are not adequate for large utility data management because they are non-scalable, do not allow for access by multiple users, involve manual data uploads, do not control consistency of data attributes, and lack visualization tools for non-GIS experts. In addition, they do not provide an end-to-end data management pipeline from data acquisition, through data integration, quality control, storage and finally to data access. The developed system in this case study was used for an end-to-end management test of large data during the testing phase and proved to perform seamlessly. Our approach could be adopted by other utility jurisdictions to manage their utility data. Such a data management system allows for automated and proper management of utility data thereby helping state and local agencies reduce utility conflicts and offset construction costs due to utility damages. This data could be combined with other rich data sources, such as financial data, and mined for valuable, hidden insights.