Freelance Data Scraping Engineer (Python)

Mindrift•New York, NY

2d•Remote

About The Position

Mindrift is seeking skilled Python Data Scraping Engineers to join the Tendem project, focusing on specialized data scraping workflows for real-world applications. This part-time remote role involves applying expertise in web scraping, data extraction, and data processing to deliver accurate and reliable results. The Mindrift platform connects specialists with innovative technology projects, aiming to develop high-quality AI technologies by combining global professional expertise with AI development efforts.

Requirements

At least 3+ years of relevant experience in data engineering, web scraping, automation, or software development.
Strong 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).
Hands-on experience with LLMs and AI frameworks to enhance automation and problem-solving.
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.

Nice To Haves

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

Responsibilities

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 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.