Senior Python Data Scraping Engineer (Freelance)

Mindrift connects specialists with innovative technology projects to help develop high-quality AI technologies globally.

• 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 with practical experience in scripting, automation, and data extraction workflows.
• Ability to solve non-trivial problems and work confidently with modern development tools.
• Experience in Python web scraping (BeautifulSoup, Selenium or similar), including dynamic content (JS, AJAX, infinite scroll) and APIs via proxies.
• Proven ability to extract data from complex structures (hierarchies, archived pages, inconsistent HTML).
• Solid background in data cleaning, normalization, and validation, delivering structured datasets (CSV, JSON, Google Sheets).
• Experience handling anti-bot mechanisms and dynamic site structures at scale.
• Experience with cloud infrastructure (AWS or equivalent) and containerization (Docker).
• Hands-on experience with LLM frameworks (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.

• 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 for 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.