Specialty Finance Senior Engineer

Responsibilities

• Design, build, and maintain a suite of dashboards that track deal/asset performance, risk, and key portfolio KPIs (e.g., collateral performance, delinquency, prepayment, loss metrics, triggers/covenants, concentrations, vintage curves).
• Build a unified web portal / internal site where dashboards, reports, and self-serve analytics tools are organized, versioned, and accessible with appropriate permissions.
• Act as point of contact for third party data providers to help normalize, and validate deal-level data.
• Support ad-hoc analysis for investment memos, portfolio reviews, and ongoing surveillance.
• Own project plans and delivery cadence: scope, milestones, dependencies, and stakeholder updates.
• Liaise with broader technology team to advocate for Specialty Finance requirements and ensure solutions meet firm standards.
• Work with investment stakeholders and convert them into clear technical specifications and prioritized roadmaps.
• Identify high-impact AI opportunities

Requirements

• 10+ years of relevant experience as a full-stack engineer specifically in specialty finance, asset-backed finance, private credit, or an adjacent buy-side setting.
• Advanced Python skills for data engineering and analytics (pandas/numpy, API integration, scripting, testing).
• Strong SQL skills and experience building analytics-ready data models with large data sets and hands-on Snowflake experience.
• Experience building dashboards and/or data applications (e.g., Streamlit/Dash, Plotly, Power BI/Tableau, or equivalent) with attention to UX for investment professionals.
• Demonstrated project management ability: translating ambiguous needs into shipped products, managing stakeholders, and delivering on timelines.
• Strong communication skills with the ability to work directly with investors and explain technical concepts clearly.

Qualifications

• Prior experience at a private credit / specialty finance buy-side firm, or direct exposure to loan-level consumer assets (mortgages, credit cards, ABS/RMBS, whole loans, or specialty lending).
• Familiarity with core credit concepts and performance metrics (delinquency/default/loss, prepayment, seasoning, vintage/cohort tracking, credit enhancement/triggers, concentration and covenant monitoring).
• Applied AI/ML experience (not necessarily deep research): using LLMs for workflow automation, document extraction, anomaly detection, or decision-support tooling.