Senior Python Data Scraping Engineer (Freelance)

Mindrift operates the Tendem project, connecting specialists with AI projects to enhance Generative AI through hybrid AI + human workflows. This role contributes to data scraping and processing within this system.

• Minimum 5+ years of experience in data engineering, web scraping, automation, or software development.
• Bachelor’s or Master’s Degree in Engineering, Applied Mathematics, Computer Science, or related field is a plus.
• Practical experience with scripting, automation, and AI-assisted workflows, including working confidently with LLMs.
• Strong experience in Python web scraping using libraries such as BeautifulSoup, Selenium, and 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.
• Background in data cleaning, normalization, validation, delivering structured datasets (CSV, JSON, Google Sheets).
• Experience managing 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 such as LangChain and OpenRouter for automation tasks.
• Strong attention to detail and commitment to data accuracy.
• Self-directed work ethic with ability to troubleshoot independently.
• English language proficiency at upper-intermediate (B2) or above level.

Nice to have:

• GitHub profile link.

• Own end-to-end data extraction workflows across complex websites, ensuring complete coverage, accuracy, and reliable delivery of structured datasets.
• Leverage internal tools (Apify, OpenRouter) alongside custom workflows to accelerate data collection, validation, and task execution.
• Ensure reliable extraction from dynamic and interactive web sources, adapting to JavaScript-rendered content and changing site behavior.
• Enforce data quality standards through validation checks, cross-source consistency, formatting specifications, and systematic verification.
• Scale scraping operations for large datasets using efficient batching or parallelization, monitor failures, and maintain stability against minor site changes.