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Mid-level Python/data-engineer demand in a metro location yields moderate candidate competition.
Core scraping and data-engineering skills are broadly transferable across industries.
Explicit 5+ years plus mandatory scraping, OCR, and Python toolset enforces strict technical filters.
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Develop scalable Python-based systems to extract, transform, and normalize data from structured and unstructured digital sources including dynamic web interfaces, PDFs, and documents.
Automate browser interactions using tools like Selenium, implement fault-tolerant extraction logic, and build data pipelines to deliver processed data in formats such as JSON or CSV.
Collaborate with cross-functional teams to optimize data workflows and prototype new tools including AI/LLM-based solutions for advanced parsing or semantic understanding.
5+ years of relevant work experience.
3+ years of experience in Python-based automation or data extraction.
Proficiency in Python including requests, BeautifulSoup (or similar), Selenium, PDF parsing libraries (e.g., PyMuPDF, PDFMiner, pdfplumber), and OCR tools (e.g., Tesseract).
Strong understanding of HTML/XML, dynamic content loading, authentication/session management, anti-bot handling, and familiarity with version control (Git) and basic CI/CD workflows.
Experienced in building robust, fault-tolerant data extraction systems handling diverse digital formats and dynamic content.
Comfortable working in agile, cross-functional teams with fast iteration cycles and collaborating with analysts and product teams to optimize data workflows.
Open to exploring and prototyping AI/LLM-based methods to enhance text analysis and entity extraction capabilities.