Job Description
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.
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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.
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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.
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What you’ll do:
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- 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.
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Qualifications:
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- 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.
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Experience with low-latency or other performance-critical systems is a plus.