Quant Trading Python Engineer

Susquehanna International

Dublin

On-site

EUR 90,000 - 150,000

Full time

3 days ago
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Job summary

Susquehanna International Group is looking for a Python software engineer to maintain and enhance our quantitative study environments used by researchers and traders. You will build and optimize Python-based tools that power fast data analysis and robust studies.

You will collaborate with traders, researchers and other engineers to deploy scalable data pipelines and production-ready software, embracing agile practices and a bias for practical, high-performance solutions.

Qualifications

  • Degree in Computer Science or a related technical field.
  • Experience delivering distributed, server-side software with measurable outcomes.
  • Proficient in Python and its scientific stack.
  • Experience across the full software development life cycle and agile methodologies.
  • Willing to learn new technologies and propose practical solutions.
  • Strong communication and collaboration with researchers and engineers.

Responsibilities

  • Maintain and enhance a Python-based platform enabling researchers to interact with trading systems.
  • Work with traders and researchers to enable fast data analysis and study pipelines.
  • Develop data pipelines handling large datasets in production quality environments.
  • Contribute to system design and production deployment of software.

Skills

Python
Distributed systems
Server-side development
Agile development
CI/CD pipelines
NoSQL databases

Education

Bachelor's degree in Computer Science

Tools

Linux
Windows
Git

Job description

Susquehanna International Group is looking for a Python software engineer to maintain and enhance our quantitative study environments used by researchers and traders. You will build and optimize Python-based tools that power fast data analysis and robust studies.

You will collaborate with traders, researchers and other engineers to deploy scalable data pipelines and production-ready software, embracing agile practices and a bias for practical, high-performance solutions.

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