Argentine Tango Events Database Creation
Budget: $250 – $750 USD
Job Overview
I am seeking a highly skilled data extraction and lead generation specialist to build a comprehensive, deduplicated global database of Argentine Tango events and Milongas. This project involves scraping specialized directories that utilize complex dynamic loading, infinite scroll, and "lazy loading" structures.
Scope of Work
Extract and clean approximately 2,500–3,000 Festivals/Marathons and 20,000–50,000 local Milongas worldwide.
Target Data Fields (Excel/CSV format)
• Event Info: Name, Location (City, Country, Venue), Dates or Recurring Days.
• Event Type: Categorize as: Festival, Marathon, Encuentro, Weekend, Seminar/Workshop, Special Milonga, or Local Milonga (Recurring).
• Contact Info: Official Website URL, Facebook Page/Group Link.
• Organizer Details: Name, Contact Email, and Phone/WhatsApp number.
• Ratings: Include "Rating Score" where available from specific sources.
Data Sources
• Global Directories: 4-5 major international Tango directories (utilizing React/infinite scroll).
• Regional Hubs: 15+ city-specific calendars (Berlin, NYC, Paris, etc.).
• Widget Extraction: Ability to extract data from embedded Google Calendars and Facebook "Events" tabs is mandatory.
• Note: Specific URLs will be shared with shortlisted candidates to ensure bidding accuracy.
Technical Requirements & Quality Control
• Advanced Scraping: Must use Python (Selenium/Playwright/Scrapy) to bypass "lazy loading" and shadow DOMs.
• Data Enrichment: Use tools like Apollo, Clay, or Hunter.io to find missing professional contact details.
• Deduplication & Fuzzy Matching: * The Unique Key Rule: Merge records based on (Name + City + Venue + Weekday).
o Logic: No duplicate rows for weekly events. Use Fuzzy Matching (e.g., Levenshtein distance) to merge records between different sources.
• Recency Filter: Only "Active" 2025–2026 data. Historical data is not accepted.
How to Apply
1. Tools: Which Python libraries/frameworks will you use for infinite scroll and "Fuzzy Matching"?
2. MANDATORY TEST: Provide a 5-row sample of active Milongas in Buenos Aires (including Phone/WhatsApp) to prove you can access full daily lists from major hubs.
3. Quote: State your fixed price for the completed, cleaned, and verified database.
I am seeking a highly skilled data extraction and lead generation specialist to build a comprehensive, deduplicated global database of Argentine Tango events and Milongas. This project involves scraping specialized directories that utilize complex dynamic loading, infinite scroll, and "lazy loading" structures.
Scope of Work
Extract and clean approximately 2,500–3,000 Festivals/Marathons and 20,000–50,000 local Milongas worldwide.
Target Data Fields (Excel/CSV format)
• Event Info: Name, Location (City, Country, Venue), Dates or Recurring Days.
• Event Type: Categorize as: Festival, Marathon, Encuentro, Weekend, Seminar/Workshop, Special Milonga, or Local Milonga (Recurring).
• Contact Info: Official Website URL, Facebook Page/Group Link.
• Organizer Details: Name, Contact Email, and Phone/WhatsApp number.
• Ratings: Include "Rating Score" where available from specific sources.
Data Sources
• Global Directories: 4-5 major international Tango directories (utilizing React/infinite scroll).
• Regional Hubs: 15+ city-specific calendars (Berlin, NYC, Paris, etc.).
• Widget Extraction: Ability to extract data from embedded Google Calendars and Facebook "Events" tabs is mandatory.
• Note: Specific URLs will be shared with shortlisted candidates to ensure bidding accuracy.
Technical Requirements & Quality Control
• Advanced Scraping: Must use Python (Selenium/Playwright/Scrapy) to bypass "lazy loading" and shadow DOMs.
• Data Enrichment: Use tools like Apollo, Clay, or Hunter.io to find missing professional contact details.
• Deduplication & Fuzzy Matching: * The Unique Key Rule: Merge records based on (Name + City + Venue + Weekday).
o Logic: No duplicate rows for weekly events. Use Fuzzy Matching (e.g., Levenshtein distance) to merge records between different sources.
• Recency Filter: Only "Active" 2025–2026 data. Historical data is not accepted.
How to Apply
1. Tools: Which Python libraries/frameworks will you use for infinite scroll and "Fuzzy Matching"?
2. MANDATORY TEST: Provide a 5-row sample of active Milongas in Buenos Aires (including Phone/WhatsApp) to prove you can access full daily lists from major hubs.
3. Quote: State your fixed price for the completed, cleaned, and verified database.
Related categories:
Python
Data Entry
Excel
Web Scraping
Data Mining
Scrapy
Data Extraction
Selenium