Data Engineer - Systematic Commodities Hedge Fund

Moreton Capital Partners; Ciudad de México; Presencial; MXN 858.664 - 1.202.129; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Competitive stipend/salary based on experience

Hands-on experience with distributed data pipelines

Mentorship from senior engineers

Descripción de la vacante

A systematic commodities hedge fund is seeking Data Engineers to design and enhance their data infrastructure. The role involves creating data pipelines, building ETL workflows with Python, and collaborating with senior engineers on innovative projects. Ideal candidates will possess strong programming skills, particularly in Python and SQL, and have a keen interest in large-scale data systems and cloud computing. The position offers a competitive salary and opportunities for mentorship in an inclusive work culture.

Formación

• Self-starter with curiosity and attention to detail.
• Interest in large-scale data systems and cloud computing.

Responsabilidades

• Designing and maintaining data pipelines for various datasets.
• Building ETL workflows using Python and orchestration tools.
• Structuring data warehouses and APIs for efficient querying.
• Developing monitoring systems for data quality.
• Assisting in cloud deployments and automation of data ingestion.

Conocimientos

Strong programming skills in Python

Familiarity with SQL

Understanding of data structures

Attention to detail

Herramientas

Airflow

Docker

AWS

Snowflake

Pandas

Polars

Descripción del empleo

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

• 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

Bonus points for

• 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

Benefits

• 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