Stas Guk

Stas давно не был на сервисе.

Но на Freelancehunt также работают 783 фрилансера в категории Парсинг данных, которые профессионально и в срок выполнят проект любой сложности.

Вилкавишкис, Литва

1 год назад

свободен для работы

на сервисе 3 года

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Web/Data Scraper Engineer

Skilled Web/Data Scraper Engineer with 4 years of experience in designing, developing, and optimizing web scraping systems to collect, process, and analyze large-scale data. Proficient in utilizing Python-based frameworks (e.g., Scrapy, BeautifulSoup) and headless browsers (e.g., Puppeteer, Selenium) for automated data extraction from complex web structures. I adept at implementing robust scraping solutions for structured and unstructured data, while ensuring data quality and integrity through intelligent parsing and validation. Strong knowledge of API integration, data pipelines, and ETL processes, with experience in scaling scraping infrastructure using cloud services such as AWS. Effective at collaborating with cross-functional teams to deliver actionable insights that drive business decisions.

Key Skills:

* Web Scraping & Crawling (Scrapy, Requests, Puppeteer, Playwright, anti-detect browser, captcha solution)

* Data Pipelines & ETL Workflows

* API Integration & Automation

* Database Management (MongoDB, PostgreSQL, MySQL)

* Data Analytics & Visualization

Навыки и умения

Программирование

24 644 UAH
Service for collect data from TripAdvisor resource

Парсинг данных

Developed a highly efficient web scraper to extract reviews, ratings, and user data from TripAdvisor for in-depth analysis of customer feedback and trends. Built using Python with Scrapy, the scraper automates data extraction from multiple pages, capturing structured data such as information about restaurants. Integrated robust error handling and retry mechanisms to ensure reliable scraping from dynamic and paginated web content. The scraped data is stored in a MongoDB database for further analysis and visualization. Additionally, the solution includes support for API integration and can scale using cloud infrastructure (AWS/GCP) for larger datasets.

Key Features:
* Automated scraping of reviews, ratings, and user data
… * Efficient handling of paginated and dynamic web content
* Data storage and analysis using MongoDB
* Scalable and optimized for large datasets
* API support for seamless data integration

Активность

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