Senior Python Data Scraping Engineer (Freelance)

Mindrift is a platform connecting specialists with technology projects, aiming to develop high-quality AI technologies by combining professional expertise globally with advanced AI development.

• At least 5+ years of relevant experience in data engineering, web scraping, automation, or software development.
• Bachelor’s or Master’s Degree in Engineering, Applied Mathematics, Computer Science, or related technical fields is a plus.
• Strong technical foundation and practical experience with scripting, automation, and data extraction workflows.
• Experience in Python web scraping (using BeautifulSoup, Selenium, or similar), including dynamic content (JavaScript, AJAX, infinite scroll) and APIs via proxies.
• Ability to extract data from complex structures like hierarchies, archived pages, or inconsistent HTML.
• Background in data cleaning, normalization, and validation to deliver structured datasets (CSV, JSON, Google Sheets).
• Experience handling anti-bot mechanisms and dynamic site structures at scale.
• Experience with cloud infrastructure (e.g., AWS) and containerization (Docker).
• Hands-on experience with LLM frameworks such as LangChain, OpenRouter, or similar applied to automation tasks.
• Strong attention to detail and commitment to data accuracy.
• Self-directed work ethic with ability to troubleshoot independently.
• English proficiency upper-intermediate (B2) or above required.

Nice to have:

• A link to GitHub portfolio or projects.

• Own end-to-end data extraction workflows across complex websites, ensuring complete coverage, accuracy, and reliable delivery of structured datasets.
• Leverage available tools and custom workflows to accelerate data collection, validation, and task execution while meeting defined requirements.
• Ensure reliable extraction from dynamic and interactive web sources, adapting approaches as needed to handle JavaScript-rendered content and changing site behavior.
• Enforce data quality standards through validation checks, cross-source consistency controls, adherence to formatting specifications, and systematic verification prior to delivery.
• Scale scraping operations for large datasets using efficient batching or parallelization, monitor failures, and maintain stability against minor site structure changes.