Data Engineer - Systematic Commodities Hedge Fund

Moreton Capital Partners; Ciudad de México; Presencial; MXN 400,000 - 600,000; Jornada completa

Descripción de la vacante

A systematic commodities hedge fund in Mexico City is seeking a Data Engineer to build and optimize data infrastructure. This role involves designing data pipelines, building ETL workflows, and structuring data warehouses. The ideal candidate has strong Python programming skills, familiarity with SQL, and an interest in cloud computing. You'll work closely with senior engineers and contribute to projects that impact trading research and execution. A competitive stipend is offered based on experience.

Servicios

Competitive stipend/salary based on experience

Formación

• Strong programming skills in Python and familiarity with SQL.
• Understanding of data structures, algorithms, and software engineering best practices.
• Interest in large-scale data systems and cloud computing.

Responsabilidades

• Design and maintain data pipelines for collecting and transforming datasets.
• Build ETL workflows using Python and orchestration tools.
• Structure data warehouses and APIs for efficient analysis.

Conocimientos

Python programming

SQL familiarity

Understanding of data structures and algorithms

Cloud computing

Experience with Airflow

Experience with Docker

Exposure to financial data

Herramientas

Snowflake

Pandas

Polars

Descripción del empleo

Data Engineer - Systematic Commodities Hedge Fund

Moreton Capital Partners is a systematic commodities hedge fund preparing to launch live trading across global futures markets. Our research and trading systems rely on robust, scalable data infrastructure. We are looking for Data Engineers to help us design, build, and optimize that infrastructure alongside senior engineers and the CIO.

Key Responsibilities
You’ll work on projects such as:

• Designing and maintaining data pipelines to collect, clean, and transform market and alternative datasets (e.g., futures, options, weather, satellite, fundamentals).
• Building ETL workflows using Python (pandas/polars) and orchestration tools such as Airflow or Prefect.
• Structuring data warehouses and APIs (SQL, Snowflake, or similar) for efficient query and analysis.
• Developing data quality and monitoring systems for latency, completeness, and integrity.
• Assisting in cloud deployments (AWS, Docker) and automation for data ingestion and versioning.
• Collaborating with Quant Researchers to make research datasets reproducible and production-ready.
• Contributing to internal documentation and code standards to ensure long-term maintainability.

Requirements

• Strong programming skills in Python and familiarity with SQL.
• Understanding of data structures, algorithms, and software engineering best practices.
• Interest in large-scale data systems, cloud computing, or distributed processing.
• Self-starter with curiosity and attention to detail.
• Experience with Airflow, Docker, or AWS.
• Familiarity with Snowflake, Polars, or Pandas workflows.
• Exposure to financial or time-series data.
• Understanding of CI/CD, version control, or testing frameworks.
• Real-world impact: Help build data systems that directly feed institutional-grade trading research and live execution.
• Technical depth: Gain hands-on experience with distributed data pipelines, cloud infrastructure, and production data engineering.
• Mentorship: Work closely with senior engineers, the CIO, and Quant Researchers on live projects.
• Collaborative culture: Inclusive, high-trust team that values initiative and learning.
• Compensation: Competitive stipend/salary based on experience.

Data Engineer Systematic Commodities Hedge Fund • Mexico City, CDMX, MX