Direct-to-consumer baby and kids apparel with daily deals model
PatPat operates a B2C fashion e-commerce business centered on babies, kids, and family matching outfits, sourcing directly from manufacturers to offer daily-rotating deals. The hiring mix is heavily weighted toward marketing (21 roles) and operations (7), reflecting a demand-generation and supply-chain–focused operation; active projects center on sales forecasting, cost control, and inventory optimization, while pain points cluster around conversion rate and logistics—typical signals of a growth-stage retailer optimizing unit economics and fulfillment velocity.
Notable leadership hires: Head of Furniture, Amazon Operations Director
PatPat is a direct-to-consumer fashion retailer founded in 2014, headquartered in Mountain View, CA, serving mothers and families with baby clothing, kids apparel, family matching outfits, and home goods. The business model emphasizes daily product launches and limited-time deals, with inventory sourced directly from top manufacturers to maintain competitive pricing. The company operates across web and mobile, with a tech stack anchored in AWS, Shopify, and a full suite of first-party and advertising tools (Google Ads, Meta Ads Manager, Amazon Advertising). Current operational focus spans China hiring, cross-functional product launches, and cost-management initiatives.
PatPat runs on Shopify for its storefront, paired with AWS for cloud infrastructure, Tableau and Power BI for analytics, and Oracle/SAP for backend enterprise operations.
PatPat is actively hiring in China, with notable leadership openings including Head of Furniture and Amazon Operations Director roles.
PatPat's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.