Software Engineer - Systematic Commodities Hedge Fund

Moreton Capital Partners; Ciudad de México; On-site; MXN 600,000 - 1,200,000; Full time

Benefits offered by this job

Health benefits

Job summary

Moreton Capital Partners seeks a Software Engineer to build data pipelines and backend services supporting research, trading, and investment decisions. You’ll own data models and a Snowflake-based warehouse, integrating internal systems with external APIs and driving AI-enabled workflows.

Strong Python and SQL skills are required, along with experience in Pandas/Polars and API development. You’ll work with Quant Researchers and the CIO in a fast-moving environment and contribute to testing and

Qualifications

• BS degree in CS/CE or related computational field.
• Strong Python and SQL with data pipelines experience.
• Experience with Pandas/Polars and API development.

Responsibilities

• Build and maintain data pipelines ingesting market and alternative datasets.
• Develop backend services and APIs for research workflows.
• Create internal trading, PM, P&L, risk, and ops systems.
• Design data models and Snowflake-based warehouse infrastructure.
• Integrate apps with internal systems and external APIs.
• Advance LLM-driven workflows and AI tooling.
• Implement data quality checks, monitoring, and alerts.
• Collaborate with Quant Researchers and CIO to translate requirements.
• Improve testing, docs, and overall engineering practices.

Skills

Python

SQL

Pandas/Polars

FastAPI

Data modeling

Data pipelines

Git/testing

Learning new tech

Education

Bachelor's degree in Computer Science/Engineering

Tools

Snowflake

Airflow

AWS

Docker

Job description

We are seeking a talented Software Engineer to join our team. We are live trading across global commodity futures, supported by an investment process rooted in machine learning.

This is a broad engineering role and a unique opportunity to work directly with our global team, owning infrastructure that supports research, trading, and investment decision-making in a fast-moving, real-capital environment.

Key Responsibilities

• Build and maintain data pipelines ingesting market and alternative datasets, from vendor feeds and price data to unstructured data.
• Build backend services and APIs supporting internal applications and research workflows.
• Develop internal systems for trading, portfolio management, P&L, risk, research, and operations.
• Design and maintain data models and warehouse infrastructure using SQL and Snowflake.
• Integrate applications with internal systems, data providers, and third-party APIs.
• Build and improve LLM-based workflows, AI-enabled tools, agents, and structured data extraction.
• Develop data-quality checks, monitoring, and alerting to improve reliability and data integrity.
• Work closely with Quant Researchers and the CIO to translate research and business requirements into production systems.
• Improve testing, documentation, code quality, and engineering practices across the team.

Strong Python and SQL skills; clean, testable code is a must.

Experience with Pandas or Polars.

Experience building APIs with FastAPI or similar Python frameworks.

Good understanding of relational databases and data modeling.

Experience building data pipelines or other data-intensive applications.

Comfortable with Git, testing, debugging, and maintaining production code.

Comfortable working across different parts of the stack and learning new technologies as needed.

Self-starter with strong problem-solving skills, attention to detail, and the ability to work autonomously in a lean, high-ownership environment.

Bachelor’s degree in Computer Science, Computer Engineering, or another computationally intensive field.

Bonus points for

• Experience with React, TypeScript, or other modern front-end technologies.
• Experience with Snowflake and Airflow or another orchestration framework.
• Familiarity with AWS, Docker, and CI/CD.
• Experience with LLMs, agents, RAG, or AI APIs.
• Experience with financial markets, commodities, systematic investing, or time-series data.

Impact from day one: Build mission-critical systems directly used by our investment and research teams.

Direct exposure: Work alongside the CIO and Quant Researchers, with a direct line to decision-making and fast feedback on what you build.

Broad ownership: Work across data, backend systems, internal applications, AI tooling, and trading infrastructure rather than being limited to one narrow area.

Learning curve: Deep exposure to commodity markets, machine-learning research workflows, and institutional-grade trading systems.

Growth trajectory: Clear path to increased scope, responsibility, and compensation as the fund scales.

Attractive compensation: Highly competitive base salary and annual bonus that scales with the business.

Positive, inclusive, and encouraging work environment.