Freelance Python Developer for B2B Lead Scraping & Automation (Medium-Term Rental System)
Budget: €750 – €1,500 EUR
Project Description:
We're developing a new platform focused on medium-term rental properties. We need an experienced Python developer to create a modular data scraping and lead automation system. The main goal is to efficiently capture public contact data (company/person name, address, phone, email) from online business directories and Google Maps. These leads will then fuel our automated email outreach campaigns to property owners and managers.
Key Responsibilities:
Design and implement modular Python scrapers using Scrapy and/or Selenium/Playwright to extract professional contact information.
Target sources will include:
"Professionals" or "Agencies" sections of major real estate portals (e.g., Idealista.com/pro, Fotocasa.es/profesionales, Habitaclia.com/agencias, Yaencontre.com/empresas).
Google Maps (specific searches like "real estate agencies [city]", "co-living [city]", "student residences [city]", "corporate housing [city]").
Note: We are not targeting individual property listings for scraping, but rather the professional contact information of those who manage multiple properties.
Extract specific data points: company/person name, address, phone number, and email.
Implement robust strategies to handle common anti-scraping mechanisms (e.g., User-Agent rotation, appropriate delays).
Develop a data processing module to clean, normalize, deduplicate, and categorize leads into relevant property types for medium-term rentals (e.g., Apartments, Rooms, Co-living, Student Residences, Guest Houses, Corporate Housing, Apart-hotels, Vacation Rentals).
Integrate processed leads with a PostgreSQL database.
Develop a CRM integration module (CRM to be defined, e.g., HubSpot, Zoho CRM) to automatically push new leads.
Integrate with the Postmark API for automated email sending, ensuring messages are tailored to the lead's category and language (potential use of Google Translate API for detection/translation if needed).
Ensure the system is modular, scalable, and ready for cloud deployment (e.g., AWS/Google Cloud).
Must-Have Skills:
Proven experience in Python.
Strong experience with Scrapy.
Practical experience with Selenium or Playwright for dynamic site scraping (especially for Google Maps).
Solid understanding of web scraping best practices, including basic anti-bot strategies and error handling.
Experience with SQL databases (PostgreSQL preferred).
Familiarity with REST APIs (for CRM and Postmark).
Understanding of modular design principles and clean code.
Ability to work independently and communicate progress effectively.
Nice-to-Have Skills:
Experience with cloud deployment (Docker, AWS/GCP).
Familiarity with Google Translate API.
Prior experience with specific CRMs (e.g., HubSpot, Zoho).
Budget: Open to discussion based on experience and proposal.
Timeline: Please provide your estimated timeline to complete this project in your proposal.
To Apply: Please share your CV, a portfolio of relevant scraping projects, and your initial approach to this project.
We're developing a new platform focused on medium-term rental properties. We need an experienced Python developer to create a modular data scraping and lead automation system. The main goal is to efficiently capture public contact data (company/person name, address, phone, email) from online business directories and Google Maps. These leads will then fuel our automated email outreach campaigns to property owners and managers.
Key Responsibilities:
Design and implement modular Python scrapers using Scrapy and/or Selenium/Playwright to extract professional contact information.
Target sources will include:
"Professionals" or "Agencies" sections of major real estate portals (e.g., Idealista.com/pro, Fotocasa.es/profesionales, Habitaclia.com/agencias, Yaencontre.com/empresas).
Google Maps (specific searches like "real estate agencies [city]", "co-living [city]", "student residences [city]", "corporate housing [city]").
Note: We are not targeting individual property listings for scraping, but rather the professional contact information of those who manage multiple properties.
Extract specific data points: company/person name, address, phone number, and email.
Implement robust strategies to handle common anti-scraping mechanisms (e.g., User-Agent rotation, appropriate delays).
Develop a data processing module to clean, normalize, deduplicate, and categorize leads into relevant property types for medium-term rentals (e.g., Apartments, Rooms, Co-living, Student Residences, Guest Houses, Corporate Housing, Apart-hotels, Vacation Rentals).
Integrate processed leads with a PostgreSQL database.
Develop a CRM integration module (CRM to be defined, e.g., HubSpot, Zoho CRM) to automatically push new leads.
Integrate with the Postmark API for automated email sending, ensuring messages are tailored to the lead's category and language (potential use of Google Translate API for detection/translation if needed).
Ensure the system is modular, scalable, and ready for cloud deployment (e.g., AWS/Google Cloud).
Must-Have Skills:
Proven experience in Python.
Strong experience with Scrapy.
Practical experience with Selenium or Playwright for dynamic site scraping (especially for Google Maps).
Solid understanding of web scraping best practices, including basic anti-bot strategies and error handling.
Experience with SQL databases (PostgreSQL preferred).
Familiarity with REST APIs (for CRM and Postmark).
Understanding of modular design principles and clean code.
Ability to work independently and communicate progress effectively.
Nice-to-Have Skills:
Experience with cloud deployment (Docker, AWS/GCP).
Familiarity with Google Translate API.
Prior experience with specific CRMs (e.g., HubSpot, Zoho).
Budget: Open to discussion based on experience and proposal.
Timeline: Please provide your estimated timeline to complete this project in your proposal.
To Apply: Please share your CV, a portfolio of relevant scraping projects, and your initial approach to this project.