Senior Python Data Scraping Engineer
New
M
MindriftAI, Data Engineering
Open to candidates in the North America, South America, Asia and Europe.ContractSenior
Salary not disclosed
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Job Details
- Languages
- English proficiency: Upper-intermediate (B2) or above
- Experience
- 5+ years
- Required Skills
- AWSDockerPythonJavascriptSeleniumLangChain
Requirements
- At least 5+ 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).
- Demonstrated experience handling anti-bot mechanisms and dynamic site structures at scale.
- Experience with cloud infrastructure (AWS or equivalent) and containerization (Docker) as part of real workflows.
- Hands-on experience with LLM frameworks (LangChain, OpenRouter, or similar) applied to automation tasks.
- 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.
Responsibilities
- 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 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.
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