Python Web Scraper + Lead Scraper at Reliant House Buyers

Closed Posted 3 months ago Paid on delivery
Closed Paid on delivery

Job Description: Python Web Scraper + Lead Scraper (Virtual Position)

Position: Python Web Scraper + Lead Scraper (Virtual)

Company: Reliant House Buyers

About Us:

Reliant House Buyers is a prominent real estate wholesaling company specializing in identifying and securing prime real estate opportunities. Our mission is to pinpoint distressed, off-market, and undervalued properties for potential investors, contributing to their success and growth. We are currently seeking a skilled Python Web Scraper + Lead Scraper to join our team in a virtual capacity and play a crucial role in expanding our real estate wholesaling endeavors.

Role Overview:

As a Python Web Scraper + Lead Scraper, your primary objective will be to develop and manage web scraping scripts using Python to extract property data from various online sources. This position requires a proactive approach to collecting leads and analyzing data. Collaborating closely with our data analysis team, you will ensure accurate and up-to-date data collection, contributing to our comprehensive real estate wholesaling operations.

Key Responsibilities:

Create, maintain, and optimize Python web scraping scripts to extract property data, including crucial details about distressed properties, off-market opportunities, ownership information, and more.

Utilize web scraping libraries and frameworks such as Beautiful Soup, Scrapy, or Selenium to automate data extraction from a variety of real estate websites, listing platforms, and databases.

Implement error handling and data validation mechanisms to guarantee the precision and reliability of collected data.

Work collaboratively with our data analysis team to refine, structure, and organize scraped data, facilitating further analysis and strategic decision-making.

Monitor changes in website structures, adjusting scripts to ensure consistent data extraction despite potential site updates.

Identify opportunities for process enhancement, suggesting improvements to streamline and improve the efficiency of web scraping operations.

Contribute to the formulation of lead generation strategies by identifying key parameters and criteria for potential investment properties.

Stay informed about industry trends, best practices, and emerging technologies in web scraping and real estate data collection.


Proficiency in Python programming, including substantial experience with web scraping libraries such as Beautiful Soup, Scrapy, or Selenium.

Strong grasp of HTML, CSS, and web page structure, enabling effective data extraction from diverse website layouts.

Demonstrated expertise in developing and maintaining web scraping scripts for data collection purposes.

Keen attention to detail and an exceptional ability to ensure data accuracy and consistency.

Problem-solving prowess and a knack for adapting to changing website structures and challenges.

Familiarity with real estate industry concepts, property data sources, and relevant online platforms is advantageous.

Excellent communication skills, allowing for seamless collaboration within cross-functional teams.

Application Instructions:

To apply for this virtual position, please visit [Online Jobs PH Link] and submit your application through the platform. In the subject line of your application, include the words "Detail Oriented" to indicate your attention to detail.

Reliant House Buyers is dedicated to promoting diversity and encourages candidates from various backgrounds to apply

Python Web Scraping Data Mining Data Engineer Data Extraction

Project ID: #37498019

About the project

24 proposals Remote project Active 1 mo ago

24 freelancers are bidding on average $149 for this job


Hello! As an adept Python developer with extensive experience in web scraping using libraries like Beautiful Soup and Scrapy, I offer a comprehensive skill set to contribute effectively to Reliant House Buyers' real es More

$140 AUD in 4 days
(278 Reviews)

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(53 Reviews)

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(7 Reviews)

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(10 Reviews)

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(4 Reviews)

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(3 Reviews)

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(0 Reviews)

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(0 Reviews)

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(0 Reviews)

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