Refactored Job Search Automation Pipeline

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📅 2025-07-07 — Session: Refactored Job Search Automation Pipeline

🕒 01:05–01:55
🏷️ Labels: Automation, Pipeline, Python, Data Processing, SERP
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The primary goal of this session was to enhance the job search automation pipeline by refining its architecture, addressing weaknesses in modular product structure, and implementing key scripts for data processing and transformation.

Key Activities

  • Database Schema and Relationships: Explored the database schema for the SERP scraper, detailing tables and relationships.
  • Modular Product Structure: Identified weaknesses in the modular design, including issues with data validation and error tracking.
  • Pipeline Architecture Refinement: Analyzed and refined the job search automation pipeline, breaking it into logical stages and improving modularity.
  • Monolithic Logic Split: Planned the architectural breakdown of monolithic logic into distinct scripts.
  • Python Scripting: Developed and executed scripts for fetching SERP data, labeling and scoring job domains, and converting CSV to JSONL format.
  • PromptFlow Error Resolution: Addressed local path issues in PromptFlow, ensuring smooth local execution.

Achievements

  • Successfully refined the pipeline architecture for better modularity and reusability.
  • Implemented key scripts for data fetching, processing, and transformation, enhancing automation capabilities.
  • Resolved critical PromptFlow execution issues, improving workflow efficiency.

Pending Tasks

  • Integrate actual scraping logic into the 01_fetch_serp.py script.
  • Define and implement batching rules for SERP data processing.

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