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IMC

Software Engineer – BP&A

Chicago, United StatesFrom $180kfull-timemidAdded today

About this role

Join IMC's BP&A team as a Software Engineer to build backend systems and data tools that power business intelligence across finance, trading, and operations. You'll develop dashboards, optimize data pipelines, and collaborate globally with a focus on turning complex data into actionable insights for the organization.

What you'll do

  • Own development, maintenance, and automation of backend systems and database solutions
  • Collaborate with BI developers and stakeholders to establish best practices and support the BP&A ecosystem
  • Build efficiency-focused tools that streamline data processing and improve performance
  • Design and develop dashboards and visualization tools for global stakeholders
  • Work with large-scale data pipelines to enable storage, retrieval, and analysis for finance operations
  • Partner with finance, trading, and middle office teams to translate business needs into technical solutions

What they're looking for

  • Python
  • SQL
  • React (preferred)
  • Agentic coding tools (Claude Code, Codex, Cursor)
  • CI/CD pipelines (GitLab CI, Jira)
  • Docker and Kubernetes
  • Cloud platforms (AWS preferred)
  • Data pipeline and ETL experience

Benefits

  • Discretionary bonus eligibility
  • Paid leave
  • Insurance coverage
  • Global team collaboration across Chicago, Amsterdam, and Sydney
  • Work on high-impact business intelligence systems
  • Exposure to quantitative finance and ML engineering
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IMC

IMC builds trading technology and financial systems powered by software, machine learning, and hardware engineering. The company is hiring interns and graduate-level engineers and researchers across software, machine learning, and hardware disciplines to develop trading algorithms, research strategies, and collaborative technology solutions.

View all jobs at IMC

Likely interview questions

  • Walk us through a backend system you built from scratch—what were the key design decisions and trade-offs?
  • Describe your experience optimizing data pipelines or SQL queries for large datasets. What was the performance improvement?
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