Project Title:
HireMatch Pro
Project Description:
HireMatch Pro is a sophisticated job-matching system designed to enhance hiring quality and reduce turnover by utilizing an extensive dataset of over 1.5 million rows from Glassdoor job reviews. This system leverages advanced data warehousing and ETL pipelines to match job seekers with ideal positions based on detailed company reviews and job satisfaction metrics.
Key Contributions:
● It developed and optimized an ETL pipeline using PostgreSQL to efficiently manage and process job review data.
● Implemented a user-friendly web interface with Flask, allowing users to input job preferences and receive tailored job matches.
● Integrated Python Psycopg for robust database interactions, enhancing data retrieval and update operations within the PostgreSQL environment.
Impact:
● Our system is designed to significantly enhance the precision of matching job seekers with positions. This is expected to increase job satisfaction, improve the quality of hires, reduce employee turnover-related expenses, and foster career success.
● It provided a scalable solution that supports expanding data sources and geographical coverage, ready to adapt to the dynamic needs of the global job market.
Technologies Employed:
● Data warehouse: Python Psycopg, pgAdmin
● ETL pipeline: PostgreSQL
● User interface: Flask
● Containerization: Docker
● Planned future enhancements include Natural Language Processing (NLP) for deeper sentiment analysis and machine learning algorithms for predictive analytics.
Step 2: Narrow your search by specifying a company, here ‘Apple’ is used as an example
Step 3: If a category is not a consideration for your search, it can be skipped. Refine your search by setting the minimum company rating on a scale of 1-5; here, a minimum rating of 2 is selected
Step 4: View your search results below; each listing provides details on job title, location, ratings in various categories, and more
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