Project Overview: We are looking for an experienced backend developer to build a robust LinkedIn scraper that monitors specific job markets (specifically Chartered Accountancy roles). The tool must aggregate data from both standard Job Search results and Content Search (Posts) results. Key Requirements: Data Sources: LinkedIn Job Search (e.g., specific keywords like "Chartered Accountant" with geo-targeting). LinkedIn Content/Post Search (e.g., boolean search strings for "Articleship" or "CA Trainee"). Customization: The script must allow us to easily input/change search parameters, keywords, Boolean strings, and location IDs. Smart Filtering: Deduplication: Must self-identify and remove duplicate listings. Blacklisting: Logic to identify and exclude 3rd-party job aggregators, ensuring only direct employer listings are shown. Technical Constraints (Important): No Browser Automation: We strictly require a solution that does not use Selenium, Puppeteer, or Playwright to avoid bot detection and IP bans. Methodology: The solution should utilize HTTP requests, hidden internal APIs, or reliable 3rd-party scraping APIs (e.g., via RapidAPI) if they are cost-effective. Stealth: The system must handle headers, cookies, or proxies effectively to remain undetected. Deliverables: Python script (or similar backend language) with clean code. A configuration file for inputting search URLs/Keywords. Output in [CSV / JSON / Database] format (Please specify your preferred output). To Apply: Please explain your approach to scraping LinkedIn without browser automation. If you propose a pre-built API solution, please include estimated monthly costs.