Position Overview
Our client is on a journey to become an AI-first organization. They believe artificial intelligence will reshape the workplace by automating repetitive tasks and enabling teams to focus on higher-value, strategic work.
The Forward Deployed Engineer (Financial Engineering) will work closely with investment, finance, and operations teams to build the data infrastructure and financial computation engines that power the Portfolio Intelligence Ecosystem. This includes Protoss, a source-of-truth platform for portfolio and fund accounting, and Clearview, a portfolio foresight platform for scenario analysis, stress testing, and exit modeling.
This role sits at the intersection of financial engineering and data engineering. Strong financial domain knowledge — fund accounting, performance measurement, and portfolio analytics — is as important as engineering fundamentals. The majority of the work involves building clean, auditable data models and financial computation engines, and the intelligence AI layer on top of it.
Responsibilities
● Partner with finance, investment, and operations teams to scope and build financial data infrastructure, from source system ingestion (Allvue, eFront, etc) to a unified, auditable data layer.
● Implement core portfolio and fund accounting calculations — NAV, IRR, TVPI, DPI, MOIC, management and performance fees — with full auditability and reconciliation logic.
● Build financial computation engines for scenario analysis and portfolio stress testing: interest rate shocks, valuation haircuts, exit timing, and liquidity forecasting.
● Develop performance attribution models that decompose portfolio returns by driver, vintage, geography, and strategy.
● Implement VaR and risk analytics adapted for private markets (illiquid assets, irregular cash flows).
● Build and maintain data quality and validation frameworks that surface inconsistencies before they reach investment teams or senior leadership.
● Own end-to-end integrations between internal platforms and third-party APIs, including authentication, error handling, and ongoing maintenance.
● Present technical findings, data quality issues, and methodology decisions to non-technical stakeholders in a structured, concise way.
Qualifications
● Bachelor's degree in Financial Engineering, Computational Finance, Mathematics, Statistics, or Computer Science — or equivalent practical experience.
● 3–6 years of experience combining financial domain knowledge with hands-on data or software engineering — quant developer, data engineer at a fund, financial systems developer, or similar.
● Proven ability to build and understand VaR, stress testing frameworks, performance attribution, and portfolio optimization models.
● Proficiency in Python and SQL; hands-on experience building production-grade data pipelines from financial source systems.
● Comfortable with APIs, system integrations, and cloud data infrastructure (GCP / BigQuery preferred).
● Curious, self-directed, and biased toward action — able to run workstreams end-to-end with minimal oversight and surface blockers early.
● Preferred: CFA, FRM, or MFE; and familiarity with dbt or similar data transformation tools.
Click 'Apply Now' to submit your application or send your resume directly to [email protected]
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