Contract Role: Senior ETF Platform Developer_ Downers Grove, IL (Hybrid)

We are looking for a hands-on senior engineer to help institutionalize and extend a portfolio of business-critical ETF investment tools used by portfolio managers and quantitative investment teams. The applications are already operational and predominantly cloud-based. The priority is not a wholesale lift-and-shift, but safe, incremental improvement: reduce key-person dependency, address technical debt and scalability issues, strengthen production support, and deliver high-value capabilities from the business backlog. You will work directly with ETF portfolio managers, quants, technology and data teams in a fast-moving co-development model, preserving critical investment logic while improving engineering quality and operational resilience.

• At least 7 years of software engineering experience, including recent ownership of production applications in asset management, capital markets or another data-intensive financial environment.

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• Strong hands-on Python engineering, beyond notebooks, including production services, testing, packaging, profiling and data processing with pandas, Polars or NumPy.

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• Production experience with PostgreSQL and Snowflake, including data modelling, query and workload performance, access controls and operational support.

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• Hands-on AWS experience using relevant services and patterns for secure, resilient and observable applications, for example ECS or EKS, Lambda, S3, RDS, CloudWatch, IAM, Secrets Manager and infrastructure as code.

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• Experience modernizing live business-critical applications incrementally while maintaining continuity and preserving specialist business logic.

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• Solid API, integration, CI/CD, automated testing, security, observability and support practices.

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• Ability to work closely and credibly with portfolio managers, quantitative analysts and investment-data specialists in an onsite, iterative delivery environment.

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• Clear written and verbal communication, with a practical approach to documentation and knowledge transfer.

• Design, build, test and support production-grade services and analytical applications using Python, PostgreSQL, Snowflake and AWS.

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• Enhance workflows supporting ETF portfolio construction, basket creation and optimization, create-redeem activity, rebalancing, tracking error and risk analysis, backtesting and tax-aware decision support.

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• Refactor or modularize independently evolved tools without disrupting daily investment activity or losing embedded business-critical logic.

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• Use appropriate Python libraries such as pandas, Polars and NumPy, with clear testing, packaging, dependency management and performance practices.

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Architecture and platform improvement

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• Introduce clear APIs and service boundaries, reusable components and consistent integration patterns across the toolset.

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• Improve scalability, resiliency, identity and access controls, secrets management, observability, logging and alerting.

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• Contribute to retain, refactor, re-platform, consolidate, replace or retire decisions based on business criticality, technical health and delivery risk.

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• Apply AI-assisted engineering responsibly for code analysis, test generation, documentation and modernization while retaining human review and control.

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Investment data and integration

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• Build and support reliable flows for market, reference, portfolio, risk and trading data, including Bloomberg and FactSet sources where applicable.

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• Work with Snowflake data shares and downstream consumers, respecting vendor entitlements, licensing, lineage and permitted usage.

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• Improve data quality controls, reconciliation, metadata and traceability, and remove duplicated feeds or inconsistent business logic where safe.

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Delivery and production ownership

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• Work iteratively with portfolio managers and quants to refine requirements, demonstrate working software and release incremental value.

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• Create automated unit, integration, regression and data-quality tests, plus CI/CD pipelines and production-readiness checks.

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• Investigate production issues, perform root-cause analysis, improve runbooks and participate in a sustainable support model across the US and UK.

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• Document architecture, interfaces, dependencies, operating procedures and key investment calculations, and transfer knowledge to permanent teams.

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