Quant Data Engineer - Investment management

Oscar Faye; New York (NY); On-site; USD 120,000 - 180,000; Full time

Job summary

Oscar Faye is partnering with a leading global investment firm to hire a Data Engineer / Quant for its quantitative strategies team, supporting an ABF and private credit business. This is a front office role working directly with traders, investors, and deal teams.

You will design scalable data pipelines, shape lakehouse architecture on Snowflake, Databricks, Spark, and AWS, and ensure data quality and governance to enable deal underwriting and portfolio monitoring.

Qualifications

• 5–10 years in data engineering, quant analytics, or fintech — ideally near structured products, private credit, or fixed income (ABS or MBS product exposure is a big plus)
• Strong Python and SQL, with production-quality data or analytical applications shipped
• Deep familiarity with data warehouse/lakehouse architecture and cloud platforms (Snowflake, AWS)
• Genuine interest in private credit and investing; comfortable with both technical and non-technical stakeholder

Responsibilities

• Design and maintain scalable data pipelines and analytical datasets across structured products and private credit workflows
• Shape the firm's data lake / lakehouse architecture on Snowflake, Databricks, Spark, and AWS
• Integrate internal and external data with strong quality, lineage, and reconciliation standards
• Partner with investment professionals to turn business needs into production data and analytics

Skills

Python

SQL

Airflow

ML/AI analytics

Tools

Snowflake

Databricks

Spark

AWS

Job description

Oscar Faye is partnered with a leading global alternative investment firm hiring a Data Engineer / Quant into its quantitative strategies group, supporting a fast-growing Asset-Based Finance (ABF — lending secured against pools of assets) and private credit business. This firm is rapidly growing and views technology and data as a core strategic imperative not an after thought or support function.

You will build the data backbone behind deal underwriting, cashflow modeling, portfolio monitoring, and risk. This is a front office role working directly with traders, investors, and deal teams, not buried three layers from the business

.

What you will do

• Design and maintain scalable data pipelines and analytical datasets across structured products and private credit workflows
• Shape the firm's data lake / lakehouse architecture on Snowflake, Databricks, Spark, and AWS
• Integrate internal and external data with strong quality, lineage, and reconciliation standards
• Partner with investment professionals to turn business needs into production data and analytics

What they are looking for

• 5–10 years in data engineering, quant analytics, or fintech — ideally near structured products, private credit, or fixed income (ABS or MBS product exposure is a big plus
• Strong Python and SQL, with production-quality data or analytical applications shipped
• Deep familiarity with data warehouse/lakehouse architecture and cloud platforms (Snowflake, AWS
• Genuine interest in private credit and investing; comfortable with both technical and non-technical stakeholder
• Airflow, ML/AI-enabled analytics, or securitization exposure is a plus.

This is a chance to build core infrastructure for one of the fastest-growing asset classes in private markets, at a firm where engineering sits inside the investment team.