Senior Python Data Scraping Engineer (Freelance)

Mindrift is a platform connecting specialists with AI projects from major tech innovators, focusing on unlocking the potential of Generative AI by leveraging real-world expertise globally. This freelance role is for the Tendem project involving specialized data scraping workflows in a hybrid AI + human system.

• At least 5+ years of experience in data engineering, web scraping, automation, or software development.
• Strong technical foundation with scripting, automation, and AI-assisted workflows.
• Experience working confidently with LLMs, systematically collecting, structuring, and validating data.
• Expertise in Python web scraping with libraries like BeautifulSoup, Selenium, handling dynamic content (JS, AJAX, infinite scroll) and APIs via proxies.
• Ability to extract data from complex structures including hierarchies and inconsistent HTML.
• Background in data cleaning, normalization, and validation, delivering structured datasets (CSV, JSON, Google Sheets).
• Experience managing anti-bot mechanisms and dynamic site structures at scale.
• Familiarity with cloud infrastructure (AWS or equivalent) and containerization (Docker).
• Hands-on experience with LLM frameworks (LangChain, OpenRouter) applied to automation.
• Attention to detail and commitment to data accuracy.
• Self-directed work ethic with independent troubleshooting capabilities.
• English proficiency at Upper-intermediate (B2) or above required.

Nice to have:

• Bachelor’s or Master’s Degree in Engineering, Applied Mathematics, Computer Science, or related fields.
• A link to GitHub.

• 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 behaviors.
• Enforce data quality standards through validation checks, cross-source consistency controls, and systematic verification before delivery.
• Scale scraping operations for large datasets using efficient batching or parallelization, monitor failures, and maintain stability against site changes.