4 days ago
Remote, SerbiaSenior
Responsibilities
- Own end-to-end data extraction workflows across complex websites and deliver accurate, structured datasets.
- Use custom workflows and available tools to accelerate data collection, validation, and task execution.
- Extract reliably from dynamic and interactive sources, including JavaScript-rendered content, infinite scroll, and changing site structures.
- Apply validation, cross-source consistency checks, formatting controls, and systematic verification to maintain data quality.
- Scale scraping operations through batching or parallelization, monitor failures, and maintain stability against minor site changes.
- Handle anti-bot mechanisms and use automation, cloud infrastructure, containerization, and LLM frameworks in scraping workflows.
Requirements
- At least 5 years of relevant experience in data engineering, web scraping, automation, or software development.
- Strong Python web-scraping experience using BeautifulSoup, Selenium or similar tools, including dynamic content and APIs via proxies.
- Ability to extract data from complex structures such as hierarchies, archived pages, and inconsistent HTML.
- Experience with data cleaning, normalization, validation, and delivery in CSV, JSON, or Google Sheets.
- Demonstrated experience handling anti-bot mechanisms and dynamic site structures at scale.
- Experience with AWS or equivalent cloud infrastructure and Docker containerization.
- Hands-on experience with LLM frameworks such as LangChain, OpenRouter, or similar tools for automation.
- Upper-intermediate English proficiency (B2) or above.
- A bachelor's or master's degree in Engineering, Applied Mathematics, Computer Science, or a related technical field is a plus.
- A GitHub link is a plus.
Benefits
- Remote, part-time freelance work.
- Estimated workload of 10–20 hours per week during active project phases; workload is not guaranteed and applies only while the project is active.
- Compensation of up to $45 per hour equivalent, depending on level and pace of contribution.
Tech Stack
Categories
Data Engineering
