Data Engineer – Centralized AI Team – Global Prop Trading Firm

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

Job summary

Mondrian Alpha is seeking a Data Engineer to design, build, and maintain scalable data pipelines that ingest market, trade, and reference data for researchers and traders. You will own data quality, latency SLAs, and be part of a fast-moving environment that values timely, accurate information.

You will collaborate with quant researchers and traders to translate data needs into robust engineering solutions, ensuring reliability of production data systems and exploring new data sources as

Qualifications

• 3+ years of data engineering experience in a production environment.
• Strong Python and SQL.
• Experience with large-scale or time-series data systems.
• Direct experience with market or financial data; non-negotiable for the role.
• Comfort working in a fast-moving, low-latency environment.

Responsibilities

• Design, build, and maintain data pipelines that ingest and normalize market, trade, and reference data at scale.
• Own data quality and latency SLAs for pipelines feeding quant research and live trading systems.
• Build and maintain time-series and event-driven data infrastructure supporting backtesting and production strategies.
• Partner directly with quant researchers and traders to understand data requirements and translate them into engineering specs.
• Monitor, troubleshoot, and improve reliability of production data systems.
• Evaluate and integrate new data sources as research needs evolve.

Skills

Python

SQL

Time-series data

Market/financial data

Low-latency environment

Data pipelines

Tools

Kafka

AWS

GCP

kdb+/Q

Job description

This role sits inside the data engineering function that feeds every signal, backtest, and live trading decision the firm makes. The pipelines this person builds and maintains carry market data, trade data, and alternative data from source to the researchers and traders who depend on it being accurate, timely, and complete. If a data feed breaks or lags during market hours, research stalls and live strategies can trade on stale information.

What You'll Do

• Design, build, and maintain data pipelines that ingest and normalize market, trade, and reference data at scale
• Own data quality and latency SLAs for pipelines feeding quant research and live trading systems
• Build and maintain time-series and event-driven data infrastructure supporting backtesting and production strategies
• Partner directly with quant researchers and traders to understand data requirements and translate them into engineering specs
• Monitor, troubleshoot, and improve reliability of production data systems
• Evaluate and integrate new data sources as research needs evolve

Must-Haves

• 3+ years of data engineering experience in a production environment
• Strong Python and SQL
• Experience with large-scale or time-series data systems
• Direct experience with market or financial data. This is non-negotiable for the role
• Comfort working in a fast-moving, low-latency environment

Nice-to-Haves

• Experience with kdb+/q or similar time-series databases
• Exposure to streaming systems like Kafka
• Cloud infrastructure experience (AWS or GCP)
• Background supporting quant research or systematic trading teams

Why This Role

This is a small, high-leverage data engineering team where the pipelines this person owns are directly load-bearing for the firm's trading and research output. Compensation is top of market, structure is flat, and the pace matches a firm that trades in real time. For a data engineer who wants their infrastructure decisions to show up directly in trading performance, this is that.