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Senior Webscraping & Data Engineer

38k - 48k PLN/ mies.UoP
SeniorFull-time·Umowa o pracę
#427092·Dodano 3 dni temu·1
Źródło: nofluffjobs.com
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Tech Stack / Keywords

Data engineeringWeb ScrapingPythonpandasHTMLJavaScriptAPIData pipelinesAlgorithmsData structuresData qualityTroubleshootingProblem-SolvingCommunication skillsCollaboration skillsAlternative dataAirflowKafkaDockerKubernetesAWSLLMMCPaudio/video data

Wymagania

  • 6+ years of experience in software or data engineering.
  • Strong experience with web scraping, crawling, and automated data collection.
  • Excellent Python skills, including pandas.
  • Strong SQL and database knowledge.
  • Good understanding of HTML, JavaScript, APIs, and web technologies.
  • Knowledge of networking concepts related to web data collection.
  • Experience building data pipelines.
  • Understanding of algorithms, data structures, and data quality.
  • Experience with large-scale or distributed data collection.
  • Strong troubleshooting and problem-solving skills.
  • Interest in AI/LLMs and agentic technologies.
  • Strong communication and collaboration skills.

Nice to have:

  • Experience with alternative data in finance or research.
  • Background in data-intensive organizations.
  • Experience building tools for researchers, analysts, quants, or data scientists.
  • Knowledge of Airflow, Kafka, Docker, or Kubernetes.
  • Experience with AWS or other cloud platforms.
  • Familiarity with LLM tools, MCPs, agents, or autonomous workflows.
  • Experience with audio/video data.
  • Knowledge of statistics and data analysis.

Obowiązki

  • Design and develop scalable web scraping and data collection systems.
  • Build scrapers and crawlers for websites with different structures and technologies.
  • Collect data from websites, APIs, and dynamic online sources.
  • Clean, transform, and standardize raw data.
  • Improve accuracy, speed, coverage, and reliability of scraping solutions.
  • Troubleshoot website changes, data quality, and performance issues.
  • Build automated pipelines for processing collected data.
  • Explore AI/ML and agentic technologies for extraction and validation.
  • Support infrastructure for large-scale data collection.
  • Implement monitoring and data quality controls.
  • Work with researchers and analysts to understand data needs.
  • Explore new technologies and hard-to-source data.
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