Quant Developer – Systematic Commodities Trading

Moreton Capital Partners; Ciudad de México; On-site; MXN 800,000 - 1,400,000; Full time

Job summary

Moreton Capital Partners is seeking a Quant Developer to join its live trading team across global commodity futures. You will build data pipelines, implement ML-driven workflows, and improve backtesting and research tooling in a fast, capital-intensive environment.

You will work with researchers and the CIO to productionize research ideas, deploy signals, and monitor data quality, latency, and model drift, leveraging Python, SQL, AWS, and containerized tooling.

Qualifications

• Fluency in Python and SQL; clean, testable code required.
• Data engineering experience with Airflow, Snowflake, and Pandas/Polars.
• Experience with systematic trading, backtesting, or market data pipelines.
• AWS cloud, Docker, and CI/CD familiarity.
• Self-starter with autonomous, high-ownership work style.
• Bachelor in CS/Comp-Eng; finance minor preferred.

Responsibilities

• Build and maintain data pipelines ingesting futures and datasets.
• Develop LLM-based workflows to support research and tooling.
• Enhance backtesting framework with simulations and performance analysis.
• Support research tooling, experiment tracking, and artifact storage.
• Set up ML compute in cloud and on-prem environments.
• Deploy signals into live trading stack with CI/CD and monitoring.
• Create dashboards and alerts for data quality and model drift.
• Collaborate with researchers to turn hypotheses into tests.

Skills

Python

SQL

Data engineering

Airflow

Snowflake

Pandas

Polars

AWS

Docker

CI/CD

Autonomous workstyle

Education

Bachelor's degree in CS/Comp-Eng

Finance minor (preferred)

Tools

Airflow

Snowflake

Pandas

Polars

AWS

Docker

CI/CD

Job description

Moreton Capital Partners is seeking a Quant Developer to join its live trading team across global commodity futures. You will build data pipelines, implement ML-driven workflows, and improve backtesting and research tooling in a fast, capital-intensive environment.

You will work with researchers and the CIO to productionize research ideas, deploy signals, and monitor data quality, latency, and model drift, leveraging Python, SQL, AWS, and containerized tooling.