Challenge

The platform relied on aggregating job listings from over 1,000 different university and company websites. The existing solution struggled with scale and maintenance; scraping scripts were brittle, difficult to configure, and required constant manual intervention when target websites changed their layouts. This unreliability threatened the quality of data available to users.

Solution

I engineered a robust, centralized scraping and synchronization system designed for high availability and easy configuration.

• Scalable Architecture: Built a distributed system capable of processing 1,000+ distinct websites concurrently without performance degradation.
• Configurable Parsers: Developed a flexible parsing engine that allowed non-developers to adjust scraping rules via configuration files, significantly reducing maintenance time.
• Data Integrity: Implemented automated validation pipelines to ensure 100,000+ vacancies were synced, de-duplicated, and categorized correctly in real-time.

Result

The new system automated the platform's most critical data pipeline, ensuring a constant stream of fresh content. This technical scalability allowed Graduateland to dominate the market with superior listing coverage, directly contributing to its acquisition by JobTeaser, the European leader in university recruitment.