Job Description

About Luma Financial Technologies

Founded in 2018, Luma Financial Technologies (“Luma”) has pioneered a cutting-edge fintech software platform that has been adopted by broker/dealer firms, RIA offices, and private banks around the world. By using Luma, institutional and retail investors have a fully customizable, independent, buy-side technology platform that helps financial teams more efficiently learn about, research, purchase, and manage alternative investments as well as annuities. Luma gives these users the ability to oversee the full, end-to-end process lifecycle by offering a suite of solutions. These include education resources and training materials; creation and pricing of custom structured products; electronic order entry; and post-trade management. By prioritizing transparency and ease of use, Luma is a multi-issuer, multi-wholesaler, and multi-product option that advisors can utilize to best meet their clients’ specific portfolio needs. Headquartered in Cincinnati, OH, Luma also has offices in New York, NY, Miami, FL, Zurich, Switzerland and Lisbon, Portugal. For more information, please visit Luma’s website

About the role

Luma Fintech is seeking a Full Stack Engineer to sit at the intersection of data operations and engineering. This role is embedded directly within our Data Operations department and owns the internal tooling and data schema that powers structured product data across the platform.

You will build and maintain the UI and backend systems our internal teams use to manage structured product data, and over time, evolve those systems from a full-service model to confidence score-driven targeted review, and ultimately toward a client-facing self-service data management experience.

This is a high-impact, high-ownership role for an engineer who is equally comfortable writing clean front-end interfaces, designing API layers, and reasoning about financial data schema. Experience in fintech or structured financial products is a strong plus.

What you'll do

Schema & Data Infrastructure

• Interface with and make improvements to the internal data schema for structured financial products
• Partner with data operations to translate business logic into schema design decisions, field definitions, and validation rules
• Manage database migrations, versioning, and schema documentation

Internal Tooling & UI

• Build and maintain the internal product data maintenance UI used by operations and data teams
• Design workflows that surface data confidence scores and flag records requiring human review, reducing manual review burden over time
• Ensure the UI supports efficient triage, editing, approval, and audit workflows

Platform Roadmap

• Lead the technical evolution from full-service data management to targeted review based on LLM-generated data confidence scores
• Architect and build the eventual client-facing self-service data management layer, including permissioning, data visibility controls, and audit trails
• Collaborate with the AI engineering team to integrate model outputs (extraction results, confidence signals) into operational workflows

Engineering Collaboration

• Work cross-functionally with data operations, product, and AI engineering teams
• Participate in, technical planning, and architecture discussions
• Maintain high standards for code quality, test coverage, and documentation

Qualifications

Required

• 4+ years of professional full stack engineering experience
• Strong front-end skills: React (or equivalent modern framework), TypeScript, component design, and UX sensibility for internal tools
• Solid back-end skills: RESTful API design, Node.js or Python, and relational database work (PostgreSQL preferred)
• Hands-on experience designing, modifying, and documenting data schemas in production environments
• Comfort working in ambiguous, fast-moving environments where you help define requirements alongside stakeholders
• Strong written and verbal communication; ability to translate technical concepts for non-technical partners

Preferred

• Experience in fintech, financial data, or structured financial products (e.g., structured notes, autocallables, fixed income)
• Familiarity with LLM-integrated workflows, confidence scoring, or human-in-the-loop review systems
• Experience building client-facing data portals or self-service tooling with role-based access controls
• Prior work in data operations or operations-adjacent engineering roles