Software Engineer - Python

A leading quantitative trading firm is seeking a Software Engineer with excellent Python programming skills and experience working with large-scale datasets . You’ll build the systems, tools, and data pipelines that support complex research and power production trading applications.

The primary focus is writing efficient, production-quality Python for demanding data workflows, with an emphasis on performance, scalability, and reliability . Experience with both pandas and Polars is highly desirable , alongside the ability to optimize processing speed and memory usage as data volumes grow.

This opportunity is well suited to engineers from big tech and other data-intensive environments who want to apply their software engineering expertise to financial markets.

What you’ll do:

• Develop and optimize Python systems for processing, transforming, and analyzing large-scale datasets.
• Build reliable data and feature pipelines that support research, experimentation, and production applications.
• Improve the speed, scalability, and memory efficiency of computational workflows.
• Develop simulation and backtesting tools to evaluate strategies under realistic market conditions.
• Partner with researchers and engineers to translate ideas and prototypes into robust production systems.
• Investigate and resolve data quality, performance, and implementation issues across the development lifecycle.

Qualifications:

• 3–7 years of experience in software engineering or quantitative development
• Excellent Python coding skills , with a track record of building and maintaining production systems.
• Strong proficiency in pandas and/or Polars, ideally with practical experience in both .
• Hands-on experience handling large datasets , including efficient data manipulation, pipeline development, and performance optimization.
• Familiarity with research-oriented software, experimentation, simulation, or backtesting workflows.
• A strong foundation in probability, statistics, and time series analysis.
• An understanding of machine learning concepts and how models move from research into production, with an interest in applying these skills to systematic trading.
• The ability to collaborate effectively across research and engineering teams.

Experience with low-latency or other performance-critical systems is a plus.