Scrapy Jobs

I need every public phone number that appears on gathered into a single, well-structured Excel workbook. Please crawl the entire site, not just a few sections, and return each number alongside the key profile details that make the data usable at a glance—name, profile URL, and any other easily captured identifiers shown next to the number. A clean .xlsx with one row per profile, no duplicates, and clearly labelled columns is the only deliverable I’m expecting. If you prefer Python, Scrapy, Selenium, Beautiful Soup or a comparable stack, go ahead; I’m interested in results, not the specific toolset, as long as the script can be rerun later should the site content change. Before delivery, double-check that: • every row contains a valid phone number and url • n...

I need a senior-level specialist to harvest product data from several e-commerce sites and deliver it in a single, well-structured CSV file. The task demands production-ready techniques—think Scrapy spiders hardened with rotating proxies, Selenium or Playwright for dynamic content, and solid anti-bot countermeasures. The information I’m after is very specific: product names, prices, pictures, and SKU. Nothing less, nothing more. Your solution must run reliably at scale, cope with frequent layout changes, and leave no trace that could trigger blocks. Python is the preferred stack, but if you have a proven alternative that meets the same bar, I’m open to hearing it. To be considered, include in your proposal: • At least one example of a comparable e-commerce scrapi...

I’m expanding our Florida outreach list and need a reliable web-scraped data set of school, college, and university administrators who oversee Nursing or other Healthcare programs. You’ll pull the information directly from two source types only—official institution websites and reputable educational directories—so every entry must be traceable back to one of those pages. Here’s exactly what must land in the spreadsheet: • Institution name • Contact’s first and last name • Job title (Administrator, Director of Nursing, CTE Healthcare lead, etc.) • Verified email address • State (always Florida) Format & delivery – Send the file in Excel (.xlsx). – First progress drop: within 5 days so I can spot-c...

I need a seasoned Python developer to build a robust scraper that collects the required data and writes it straight to JSON—no additional cleaning or processing necessary. Once we begin I’ll provide the target URL(s) and any access details; for now, assume a standard public site with pagination and occasional anti-bot checks. Core expectations • Written in Python 3 using requests/BeautifulSoup or Scrapy; resort to Selenium only if there’s no lighter workaround. • Handles pagination, retries, and polite delays gracefully so the run can complete unattended. • Config file or clear constants for headers, cookies, and start URLs, letting me tweak targets without editing core logic. • Produces a single JSON file (or one file per page if that’s...

I need to build a reliable, well-structured lead list and I already know exactly what it should contain. The task is to extract contact information—email addresses, phone numbers and full mailing addresses—from three sources: company and organisation websites, their public social-media profiles, and well-known online directories. I expect the data to be gathered with a solid scraping workflow (Python, Scrapy, BeautifulSoup, Selenium or an equivalent stack is fine) and then verified so that bounced emails and dead numbers are kept to an absolute minimum. Deliverables • One CSV or Excel file with separate columns for name, company, job title, email, phone, street address, city, state, ZIP/postcode, country, source URL and date collected. • No duplicates; every...

I have a data-analysis pipeline that relies on a steady flow of fresh product images from a well-known e-commerce site. What I need is a robust scraper that can navigate the catalog, collect every product’s main and variant images, and deliver them to me neatly organized. Key points you should know: • Target: a single e-commerce platform (URL supplied after award). • Payload: high-resolution image files plus a CSV/JSON map linking each file to product ID, title, price, and category text that you extract during the same run. • Scale: thousands of products per crawl; a resumable approach is essential so partial failures don’t force a full restart. • Frequency: I’ll trigger the crawl weekly, so reusable code is a must. I’m happy with Pytho...

I need a developer to collect data from multiple public websites and deliver it in a clean, structured format. This is for legitimate data extraction from publicly available pages. I will share the target URLs and exact data fields with shortlisted candidates. Scope of work Scrape data from multiple public websites (details shared after shortlisting) Extract specific fields consistently and handle pagination/filtering where needed Normalize/clean the data (remove duplicates, consistent formatting) Export results to CSV/Excel/JSON (format to be confirmed) Provide a repeatable solution (script or small app) that I can run on demand Basic documentation: how to run it, how to adjust settings, where outputs go Quality requirements Reliable scraping with error handling and retries Resp...

I have an urgent need for a clean, well-structured dataset containing the listing agent’s first name, last name, mailing address, and phone number for well over 500 active Zillow listings. Speed is critical, but accuracy matters just as much; the final file should be ready for immediate import into my CRM. You are free to use whichever stack you prefer—Python with BeautifulSoup or Scrapy, Selenium, residential proxies, even the unofficial Zillow API—so long as rate-limits are respected and the data is complete. I don’t need property details or price history; the focus is strictly on the agent contact fields. Deliverables • CSV or XLSX with a separate column for each required field • A short read-me explaining the script or method so I can rerun it la...

Preciso de um especialista em web scraping para coletar informações específicas consultando CPF em um site. Campos necessários: - Nome completo - Data de nascimento - Endereço - E-mails - Telefones - Veículo (marca/modelo) - Ano de fabricação - Ocupação - Faixa salarial - Provável empresa Habilidades e Experiência Ideais: - Experiência comprovada em web scraping - Proficiência em ferramentas como Python, Beautiful Soup, Scrapy, ou similares - Capacidade de trabalhar com estruturas de dados complexas - Atenção aos detalhes e precisão na extração de dados - Familiaridade com questões legais e éticas de scraping de ...