wisdomtree-2 - Software Engineer

• Strong software engineering fundamentals and substantial hands-on development experience. • Strong Python skills and experience building real applications rather than only notebooks or analytical scripts. • Experience with modern web development. React, TypeScript or JavaScript experience is strongly preferred. • Significant experience using AI coding agents and copilots as part of your normal development workflow. • Experience building applications using LLMs, agents, tool calling, retrieval or related AI application patterns. • Strong practical knowledge of APIs, Git, containers and Docker. • Experience with cloud-hosted applications and CI/CD. Azure experience is useful but not essential. • A working understanding of authentication, application security, secrets, networking, logging and observability. • The ability to read an unfamiliar or imperfect codebase, understand why it works and improve it without feeling compelled to rewrite everything. • Good architectural judgment, particularly the instinct to simplify systems rather than add unnecessary abstraction. • The ability to work independently with ambiguous requirements and a strong bias toward shipping something useful. • Strong communication skills and the ability to work directly with investment professionals who may know exactly what they want to achieve but not how the software should be constructed. • An interest in financial markets and investing is important. Deep financial expertise is not required on day one, but curiosity and the ability to learn quickly are. • A bachelor's or master's degree in Computer Science, Mathematics, Engineering, Physics, Statistics, Finance or another quantitative discipline. • Databricks, Spark or modern cloud data platforms. • SQL and large financial or analytical datasets. • Docker Compose, development containers or improving complex local development environments. • Azure App Services or similar managed application platforms. • LLM evaluation, retrieval-augmented generation and agent frameworks. • Building internal developer platforms or self-service application environments. • Quantitative finance, investment research, portfolio analytics, ETFs or financial markets. • Taking an experimental or founder-built codebase and maturing it without destroying the qualities that made it useful.