Software Engineer

Key Accountabilities and Scope

• Own and evolve the CUBE platform, becoming the primary developer responsible for its architecture, codebase, developer experience, reliability and ongoing operation.
• Work directly with portfolio managers and senior investment professionals to turn loosely defined ideas into useful applications quickly.
• Use modern AI coding tools and agents extensively to prototype, build, debug and refactor software.
• Integrate the CUBE environment in the wider Wisdom tree platform.
• Take AI-generated or rapidly prototyped applications and apply sound engineering judgment so they remain understandable, maintainable and supportable.
• Simplify CUBE development environment and container architecture, particularly the local developer experience, removing unnecessary complexity where possible.
• Improve development and deployment workflows including containers, CI/CD, environment management, logging, monitoring, authentication and configuration.
• Build full-stack research applications using Python and modern web technologies.
• Develop applications that combine LLMs with structured financial data, market data, proprietary research, documents and internal knowledge.
• Build agentic workflows, research assistants, analytical tools, dashboards and workflow automations.
• Integrate applications with WisdomTree's data and technology platforms, including Databricks, databases, APIs, identity systems and cloud infrastructure.
• Debug unfamiliar systems pragmatically. You should be comfortable tracing a problem across application code, containers, APIs, authentication, networking and cloud infrastructure rather than assuming somebody else owns the next layer.
• Establish sensible engineering standards without introducing heavyweight processes that slow down experimentation.
• Document important architectural decisions, dependencies and operational procedures so that knowledge does not live with one developer.

Skills and Qualifications

Required Skills and Experience
• 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.
Desired Skills and Experience
• 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.