Associate Software Engineer
Morningstar
Chicago, IL0–2 yrs1h agovia LinkedIn
This job at a glance
- Who can apply
- New grads and up to 2 years · Bachelor's degree
- Must know
- AWSTerraformETLTroubleshootingCI/CDPython
- Work
- On-site in Chicago
- Pay
- Not stated by the employer.
- Skill demand
- Troubleshooting19%ETL13%AWS6%Terraform6%of 16 similar entry-level jobs posted in the US this week
- Listed
- Nov 10, 2026 · still open
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About the Role
Morningstar's Analytics team builds the data and quantitative models behind investor-reliant products. We are seeking an engineer with strengths in cloud infrastructure, automation, and production systems to work alongside analysts and researchers. You will help transform research into reliable, scalable, customer-facing analytics, starting with our Risk Analytics suite by supporting and improving production pipelines for risk metric computation.
Key Responsibilities
- Build and maintain cloud infrastructure on AWS using infrastructure as code (e.g., Terraform).
- Monitor, troubleshoot, and improve production data pipelines.
- Participate in an on-call rotation.
- Partner with analysts and researchers to productionize their models and calculations.
- Develop CI/CD pipelines, automated tests, and deployment tooling.
- Improve observability, data quality checks, and documentation.
Requirements & Tech Stack
- Hands-on exposure to AWS (e.g., S3, Lambda, ECS, IAM, Step Functions) through work, internships, or projects.
- Experience with infrastructure as code, ideally Terraform.
- Proficiency in Python and SQL.
- Good engineering habits: version control, testing, code review, and writing maintainable code.
- Familiarity with CI/CD practices and tooling.
- Strong communication and collaboration skills; ability to explain technical ideas clearly and listen well.
- A real interest in quantitative finance and how analytics drive investment decisions.
- Bachelor’s degree in computer science, engineering, mathematics, or a related field, or equivalent experience.
- Experience with modern data platforms (e.g., Spark, Presto/Trino/Athena, data lakes or lakehouses).
- Comfort with quantitative work: descriptive statistics, basic knowledge of linear algebra and calculus (applied to returns, volatility, correlation, regression).
- Finance coursework or knowledge of markets, investment types, risk measures, or performance attribution.
- Experience with containers (Docker) or monitoring and alerting tools.