← Back to WorksProject Detail // 2025
Automation
LinkedIn Scraper
Role
Automation Engineer
Timeline
2025
Tech Stack
ApifyAutomationNode.jsExcel
Case Study Overview
Manually searching for internships on job boards is highly time-consuming, and search filters often contaminate results with irrelevant senior roles.
01 // Context
Project Summary
Designed and ran an agentic internship search pipeline using Apify's LinkedIn scraper. Collected job listings across Software Engineering, Full Stack, AI/ML, and related categories; applied post-scrape filtering logic to clean contaminated results; exported a structured Excel workbook.
02 // Core Build
Key Features
- Automated job scraping across multiple key target queries
- Post-scrape data scrubbing scripts detecting experience level keywords
- Automatic categorization into Software Eng, Full Stack, and AI/ML
- Export to structured Excel spreadsheet matching search trackers
linkedin-scraper_challenge.log
~ $cat challenge_report.txt
Handling LinkedIn query contamination (where search filters include full-time roles in internship lists) by filtering description bodies.
SUCCESS:Resolved performance block using custom caching.
03 // Impact
Results
Successfully scraped, cleaned, and organized hundreds of potential internship targets, creating a structured tracking sheet.
100%Shipped