Daniel Reeves

Daniel Reeves is a software engineer and technical writer specializing in ecommerce infrastructure, pricing technology, and retail data systems. With more than eight years of experience working with backend systems and price intelligence technology, he focuses on the technical processes that make large-scale price monitoring, product matching, and automated repricing possible.

Daniel’s background is in backend development, with hands-on experience using Python, Go, and Node.js to build and maintain data-intensive applications. Throughout his career, he has worked with pricing data pipelines, web crawling systems, API integrations, and data normalization processes designed to collect, structure, and deliver retail data at scale.

His areas of expertise include price monitoring APIs, crawler architecture, dynamic repricing algorithms, product matching logic, and ecommerce platform integrations. Daniel is particularly interested in the challenges behind collecting reliable pricing data from complex ecommerce environments, including product variation handling, marketplace seller data, data normalization, and matching identical or comparable products across different websites.

He also has experience working with integrations involving major ecommerce platforms such as Shopify, WooCommerce, and Magento, as well as marketplace APIs including Amazon SP-API and Walmart API. His technical interests extend to API design, scalable retail data pipelines, automated data validation, and the infrastructure required to transform raw ecommerce data into actionable pricing intelligence.

At Priceva, Daniel writes about the intersection of software engineering and retail operations. His work explains the technology behind price monitoring, web scraping, marketplace integrations, dynamic pricing, and competitive intelligence, helping technical and business audiences better understand how modern pricing systems operate.