Senior Python Data Scraping Engineer (Freelance)

Mindrift operates the Tendem project, connecting specialists with innovative technology projects to develop high-quality AI technologies by combining real-world expertise from professionals across the globe with advanced AI development efforts.

• 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 experience in Python web scraping using BeautifulSoup, Selenium, or similar tools, including handling dynamic content (JS, AJAX, infinite scroll) and APIs via proxies.
• Proven ability to extract data from complex structures such as hierarchies, archived pages, and inconsistent HTML.
• Solid background in data cleaning, normalization, validation, and delivering structured datasets in formats such as CSV, JSON, and Google Sheets.
• Experience handling anti-bot mechanisms and dynamic site structures at scale.
• Experience with cloud infrastructure such as AWS and containerization with Docker.
• Hands-on experience with LLM frameworks like LangChain and OpenRouter applied to automation tasks.
• Strong attention to detail and commitment to data accuracy.
• Self-directed work ethic with ability to troubleshoot independently.
• English proficiency at Upper-intermediate (B2) or above.

Nice to have:

• A link to GitHub profile.

• 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.
• Ensure reliable extraction from dynamic and interactive web sources, handling JavaScript-rendered content and changing site behavior.
• Enforce data quality standards through validation checks, cross-source consistency controls, and adherence to formatting specifications.
• Scale scraping operations for large datasets using batching or parallelization, monitor failures, and maintain stability against site structure changes.