Senior Python Data Scraping Engineer (Freelance)

Mindrift is a platform connecting specialists with innovative technology projects focused on developing high-quality AI technologies worldwide. This role is for the Tendem project, which involves specialized data scraping workflows for real-world applications.

• 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 libraries such as BeautifulSoup and Selenium, including handling dynamic content (JavaScript, AJAX, infinite scroll) and APIs via proxies.
• Proven ability to extract data from complex structures including hierarchies, archived pages, and inconsistent HTML.
• Solid background in data cleaning, normalization, and validation, delivering structured datasets (CSV, JSON, Google Sheets).
• Demonstrated experience handling anti-bot mechanisms and dynamic site structures at scale.
• Experience with cloud infrastructure like AWS and containerization using Docker.
• Hands-on experience with LLM frameworks such as 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) level or above is required.

Nice to have:

• A link to GitHub repository demonstrating relevant work.

• 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 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.