LinkedIn Job Scraping & Data Export Pipeline

Problem

Teams often need recurring job-market or competitive-market datasets, but manual collection is slow, inconsistent, and hard to reuse.

Solution

Built a Python scraping pipeline that collects job listings, normalizes records, persists run history, and exports reusable datasets through CLI workflows.

Deliverables

• Scraping library
• CLI commands
• SQLite-backed persistence
• Export workflows
• Managed artifacts
• Documentation
• CI/docs/release automation
• Optional OpenAI enrichment

Why it matters

• Turns manual job-market research into an automated, repeatable data product
• CLI-driven — anyone on the team can run it, not just the person who built it
• SQLite history lets you track market changes over time without re-scraping from scratch
• Optional OpenAI enrichment adds structured tagging to raw scraped records