Kogan.com operates a public ecommerce platform serving millions of Australian and New Zealand shoppers across electronics, fashion, home goods, and automotive. The tech stack reveals a hybrid-cloud data architecture—SQL Server and SAP for transactional workloads, BigQuery and Snowflake for analytics, with Apache Airflow and dbt orchestrating ETL pipelines. Active projects around scalable ETL/ELT and MLOps pipeline automation, paired with pain points in data-pipeline scaling for 10M+ events, indicate a company moving beyond simple ecommerce databases toward event-driven analytics and machine learning. B2B division scaling is a near-term growth lever.
Kogan.com is Australia's largest pure-play online retailer, founded in 2006 and publicly listed. The company operates a broad product marketplace spanning consumer electronics, fashion, sports, tools, and home goods, with hundreds of thousands of daily visitors and millions of registered customers. Engineering and operations hiring is active and accelerating, concentrated in Australia. The technology foundation combines enterprise resource planning (SAP), transactional SQL Server databases, cloud data warehouses (Snowflake, BigQuery), and modern data orchestration (Airflow, dbt) to support both customer-facing ecommerce and internal supply-chain optimization.
SAP and SQL Server for transactional systems; Snowflake and BigQuery for data warehousing; Apache Airflow and dbt for ETL orchestration; React for frontend; AWS and Azure for cloud infrastructure; Salesforce for CRM.
B2B division development, scalable ETL/ELT pipeline architecture, data modeling optimization in BigQuery and Snowflake, and MLOps pipeline automation to handle 10M+ daily events.
Kogan.com'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.