Senior Python Data Scraping Engineer (Freelance)

Mindrift is a platform that connects specialists with AI projects from major tech innovators, aiming to unlock the potential of Generative AI by integrating real-world expertise globally. This opportunity is within the Tendem project as a freelance role focused on specialized data scraping workflows with a hybrid AI and human system.

• At least 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 fields is a plus.
• Strong technical foundation with practical experience in scripting, automation, and AI-assisted workflows.
• Proficient in Python web scraping using BeautifulSoup, Selenium, or similar, including handling dynamic content (JS, AJAX, infinite scroll) and APIs via proxies.
• Ability to extract data from complex structures such as hierarchies, archived pages, and inconsistent HTML.
• Experienced in data cleaning, normalization, validation, and delivering structured datasets in formats like CSV, JSON, and Google Sheets.
• Skilled in 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 such as LangChain, OpenRouter, or similar.
• Strong attention to detail, commitment to data accuracy, and ability to work independently.
• English proficiency at Upper-intermediate (B2) level or above required.

Nice to have:

• A link to GitHub portfolio.

• 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) and custom workflows to accelerate data collection, validation, and execution.
• Ensure reliable extraction from dynamic and interactive web sources, adapting for JavaScript-rendered content and site changes.
• Enforce data quality standards via validation checks, cross-source consistency controls, and systematic verification.
• Scale scraping operations for large datasets using efficient batching or parallelization while monitoring failures and maintaining stability.