Marketplace connecting homeowners with tradespeople across Australia and New Zealand
hipages Group operates a two-sided marketplace for home services, matching homeowners with tradies while selling subscription tools and advertising to trade businesses. The tech stack reflects a mature, data-driven operation: Salesforce + Tableau + Power BI for commercial visibility, Kafka + Snowflake + dbt for streaming data pipelines, and React + React Native for mobile-first consumer experience. Current hiring is heavily sales-weighted (6 of 9 open roles) with a focus on mid-level contributors, suggesting aggressive expansion of direct go-to-market despite internal pain points around enablement effectiveness and churn.
hipages Group is Australia and New Zealand's largest marketplace connecting homeowners with trusted tradespeople. Founded in 2004, the platform has facilitated over 12 million home service jobs and operates a dual-revenue model: consumer-facing job posting and discovery, plus subscription services and advertising for trade businesses. The company operates with 350+ employees distributed across Australia, New Zealand, the Philippines, and Vietnam, structured as a public company listed on the ASX (HPG). Revenue streams flow from tradies adopting digital tools and homeowners purchasing premium job visibility.
Core stack includes Salesforce for CRM, Kafka + Snowflake + dbt for data pipelines, Tableau/Power BI/Looker for analytics, React/React Native for front-end, AWS/GCP/Azure for cloud infrastructure, and Kubernetes for orchestration.
Approximately 350+ team members across Australia, New Zealand, the Philippines, and Vietnam, with headcount in the 201–500 range.
hipages Group'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.