Head of Data Engineering, SableProxy
Daniel ran price intelligence for a retail group across nine markets before joining SableProxy. He now spends most of his time helping teams re-architect crawlers whose costs grew faster than their data volume — usually by tiering exits and classifying responses instead of buying more residential traffic.
3 articles
A nine-market retailer rebuilt its price crawler with exit tiering and sticky checkout sessions, cutting residential traffic spend while keeping comparable storefront measurements.
How to build a price monitoring pipeline that produces comparable data: storefront modelling, observation points, cadence, cost tiering and the context fields most teams forget to record.
A cost-first comparison of residential and datacenter proxies: how detection actually works, what each type costs per successful row, and a tiering pattern that usually cuts the bill by two thirds.
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