Quantitative Developer

Fintal Partners; Chicago (IL); On-site; USD 120,000 - 240,000; Full time

Job summary

Fintal Partners in Chicago is seeking a Quantitative Developer who writes production-quality Python for large-scale data workflows and trading research. You will build data pipelines, feature sets, and backtesting tools that move ideas from research into live systems.

The role emphasizes performance optimization, low-latency considerations, and collaboration with researchers and engineers to productionize robust solutions for the firm’s systematic strategies.

Qualifications

• 3–7 years of experience in quantitative development or a closely related software engineering role, ideally within a trading firm or systematic investment fund.
• 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.
• A strong foundation in probability, statistics, and time series analysis.
• An understanding of machine learning concepts and their application to systematic trading.
• The ability to collaborate effectively across research and engineering teams.

Responsibilities

• Develop and optimize Python systems for processing, transforming, and analyzing large-scale financial datasets.
• Build reliable data, feature, and signal pipelines that support quantitative research and live trading.
• Improve the speed, scalability, and memory efficiency of research workflows.
• Develop backtesting and simulation tools that account for market behavior and real-world trading constraints.
• Partner with researchers and engineers to translate research ideas into robust production systems.
• Investigate and resolve data quality, performance, and implementation issues across the research-to-production lifecycle.

Skills

Python

Pandas

Polars

Data pipelines

Job description

A leading quantitative trading firm is seeking a Quantitative Developer with strong Python programming skills and experience working with large-scale datasets. This role sits at the intersection of quantitative research and software engineering, building the tools and data pipelines that move trading ideas from research into production.

The primary focus is writing efficient, production-quality Python for demanding data workflows. Experience with both pandas and Polars is highly desirable, alongside the ability to optimize performance as data volumes and computational demands grow.

What you’ll do:

• Develop and optimize Python systems for processing, transforming, and analyzing large-scale financial datasets.
• Build reliable data, feature, and signal pipelines that support quantitative research and live trading.
• Improve the speed, scalability, and memory efficiency of research workflows.
• Develop backtesting and simulation tools that account for market behavior and real-world trading constraints.
• Partner with researchers and engineers to translate research ideas into robust production systems.
• Investigate and resolve data quality, performance, and implementation issues across the research-to-production lifecycle.

Qualifications:

• 3–7 years of experience in quantitative development or a closely related software engineering role, ideally within a trading firm or systematic investment fund.
• 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.
• Familiarity with quantitative research, backtesting, and simulation workflows.
• Hands-on experience handling large datasets, including efficient data manipulation, pipeline development, and performance optimization.
• A strong foundation in probability, statistics, and time series analysis.
• An understanding of machine learning concepts and their application to systematic trading.
• The ability to collaborate effectively across research and engineering teams.

Experience with low-latency trading systems is a plus.