Trainee / Specialist – Data Mining & Web Scraping Automation (m/f/d)

Stellenbeschreibung:

Organization: IRIS – Institut für raumbezogene Infrastruktur und Raumforschung (gGmbH)

Location: Germany (On-site / Hybrid with Relocation & Housing Support)

Target Program: Opportunity Card (Chancenkarte) / Master Trainees / International Talents

Working Language: English (No initial German language skills required)

About the Research Institute:

The IRIS Institute (gGmbH) conducts applied research into spatial data systems, automated data extraction, and infrastructure transformation. We build scalable data pipelines to turn fragmented public records into structured spatial insights.

What We Offer:

We fully sponsor and support your relocation journey to Germany via the Opportunity Card framework, managing visa formalities, degree assessments, and onboarding logistics. You will benefit from organized accommodation, living allowances during training, sponsored German language classes, and hands‑on exposure to production‑grade data mining environments.

Your Role & Key Tasks:

You will design, develop, and maintain autonomous scraping routines and extraction agents for public registers, corporate filings, and official announcements. You will parse complex unstructured document formats—such as HTML, PDF, JSON, and XML—into clean, relational data structures, while monitoring data feeds and integrating incoming datasets into our core spatial repositories.

Candidate Profile & Qualifications:

Applicants should hold a university degree in Computer Science, Software Engineering, Data Analytics, or a related technical subject. You should possess solid foundations or a strong motivation to develop your skills in Python (using libraries such as Scrapy, BeautifulSoup, Selenium, or Playwright), text parsing, regex, and structured data modelling. Fluent English is required.

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NOTE / HINWEIS:
EnglishEN: Please refer to Fuchsjobs for the source of your application
DeutschDE: Bitte erwähne Fuchsjobs, als Quelle Deiner Bewerbung

Stelleninformationen

  • Veröffentlichungsdatum:

    11 Nov 2026
  • Standort:

    Remote
  • Typ:

    Vollzeit
  • Arbeitsmodell:

    Vor Ort
  • Kategorie:

  • Erfahrung:

    2+ years
  • Arbeitsverhältnis:

    Angestellt

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