Refactored and Modularized Job Search Pipeline

📅 2025-07-07 — Session: Refactored and Modularized Job Search Pipeline

🕒 01:05–01:55
🏷️ Labels: Database, Automation, Job Search, Python, Data Processing, Error Handling
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to refactor and modularize the job search pipeline for improved data processing, automation, and error handling.

Key Activities

• Database Schema Design: Outlined the database schema and relationships for the SERP scraper output, focusing on data extraction and job search.
• Weakness Identification: Analyzed the modular product structure to identify weaknesses in data integrity and processing, suggesting actionable fixes.
• Pipeline Refinement: Refined the job search automation pipeline by breaking it into logical stages and addressing weak points for better modularity.
• Architectural Planning: Developed a plan to split monolithic logic into distinct scripts, detailing their responsibilities and interactions.
• Script Development: Created and implemented Python scripts for fetching SERP data, labeling job domains, and converting CSV to JSONL formats.
• Error Resolution: Fixed a PromptFlow local path resolution error, enhancing local execution and error handling.

Achievements

• Developed a modular script 01_fetch_serp.py for SERP data processing.
• Implemented 02_label_and_score.py for domain extraction and labeling.
• Completed a script for JSONL conversion of job search results.
• Resolved PromptFlow path resolution error.

Pending Tasks

• Integrate real scraping logic into 01_fetch_serp.py.
• Define batching rules and ensure output schema consistency.
• Decide on the format for Stage 3 focusing on JSONL export.

Labels

database, automation, job search, [[Python]], data processing, error handling