Summary: Moreton Capital Partners seeks a Data Engineer to design, build, and optimize robust, scalable data infrastructure for a systematic commodities hedge fund. Highlights: 1. Design and maintain data pipelines for market and alternative datasets. 2. Build ETL workflows using Python, Airflow, or Prefect. 3. Develop data quality and monitoring systems. ### **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. 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.