Quant Engineer — Build Live Data & Backtesting Infra (SF)

Digital Waffle; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Digital Waffle in San Francisco is seeking a Quant Engineer to build the data, modelling and execution infrastructure behind quantitative strategies. This hybrid role blends engineering with research, delivering data pipelines, backtesting frameworks and production systems that live on in live trading.

You’ll partner with researchers on model implementation, feature engineering and productionisation, ensuring data quality, timing and reproducibility across the research-to-production lifecycle

Qualifications

• Strong production Python experience with Pandas/NumPy/Polars.
• Solid SQL fundamentals.
• Experience with high-performance engineering (C++, Rust or optimized Python).
• Track record building data, backtesting or execution infrastructure in quant/trading environments.
• Experience with real-world time-series data issues (survivorship, lookahead, gaps).
• Fluent with AI engineering tools to raise output.
• High autonomy and pragmatism with incomplete information.
• Willing to work in-person 5 days a week in San Francisco.

Responsibilities

• Build and maintain high-performance data pipelines in Python (Pandas, Polars, NumPy) and SQL.
• Design backtesting infrastructure and simulation environments that researchers can trust.
• Build and optimise execution and signal-processing systems with low latency in mind.
• Partner with researchers on model implementation, feature engineering and productionisation.
• Own data quality, timing and reproducibility across the research-to-production lifecycle.
• Work with modern storage and compute (DuckDB, kdb+, Parquet-based lakes or similar) depending on workload.
• Use AI tooling across your workflow to move faster.

Skills

Python

Pandas

NumPy

Polars

SQL

C++

Rust

Backtesting

Tools

DuckDB

kdb+

Parquet

Job description

Digital Waffle in San Francisco is seeking a Quant Engineer to build the data, modelling and execution infrastructure behind quantitative strategies. This hybrid role blends engineering with research, delivering data pipelines, backtesting frameworks and production systems that live on in live trading.

You’ll partner with researchers on model implementation, feature engineering and productionisation, ensuring data quality, timing and reproducibility across the research-to-production lifecycle