Job Scraper — Automated Role Discovery System
Automated daily scraping of target company career pages with Slack alerts for newly posted roles that match my criteria.
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Job Scraper — Case Study
Context: Before this project, I manually checked ~30 target company job pages once every 1–2 weeks — making it nearly impossible to see jobs within 24 hours of posting.
Objective: Build an automated system that discovers new, relevant job postings within 24 hours of posting and sends daily alerts so I can apply early and systematically.
Solution: I designed and built a custom Python-based scraper that runs daily, fetches job listings from selected companies, filters them by role criteria, and sends a consolidated Slack message listing relevant postings.
- Input layer: Hard-coded list of companies + target role descriptors.
- Execution layer: Lightweight scraping and filtering logic.
- Output layer: Slack notifications delivered daily.
Outcomes:
- Time spent checking sites: 14 hrs/week → 0 hrs
- Jobs seen within 24 hrs of posting: <10% → ~90%+
- Manual effort saved: ~100% reduction
Impact: This automation dramatically improved the cadence and efficiency of my job search, enabling me to consistently apply quickly and strategically to target roles.
Key Learnings: Clear problem definition, lean prototyping, and integrating with asynchronous delivery made this not just a technical project, but a product that materially changed my personal workflow.
AI Leverage: 100% coded using AI
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