About the Role
We are seeking a Data Engineer to join our Commodities Data Science team supporting commodities trading. In this role, you will build and maintain the data pipelines and geospatial infrastructure that turn raw vessel-movement data (AIS), port activity, and cargo flows into actionable trading signals across energy, agricultural, and metals markets.
You will work at the intersection of large-scale geospatial engineering and front-office trading, collaborating closely with portfolio managers, quantitative researchers, and fellow data scientists to deliver clean, reliable, and timely maritime datasets that directly inform investment decisions.
What You'll Do
• Design, build, and maintain robust ETL/ELT pipelines ingesting AIS and other maritime datasets, including port calls, drafts, cargo, and vessel metadata, at scale.
• Develop and optimize geospatial data models in PostGIS to support vessel tracking, route inference, port congestion, and storage/flow estimation.
• Write performant, well-tested Python and SQL to transform raw feeds into research- and trading-ready datasets.
• Engineer features and metrics—such as tonne-miles, floating storage, port dwell times, and voyage estimates—in partnership with researchers and PMs.
• Ensure data quality, lineage, monitoring, and reliability across maritime data products.
• Collaborate with the commodities desk to translate trading questions into data solutions.
Must-Haves
• 3+ years of commercial Python 3 experience, with strong production-quality coding skills rather than solely scripting experience.
• PostGIS proficiency, including spatial queries, indexing, and geospatial data modeling.
• Strong SQL proficiency, including query optimization on large datasets.
Nice-to-Haves
• Hands-on familiarity with AIS or other vessel-tracking datasets, including their structure, quirks, and limitations.
• Apache Airflow, or equivalent orchestration tooling, for scheduling and managing pipelines.
• AWS experience; Azure or GCP experience is equally welcome.
• Machine learning experience, particularly applied to geospatial or time-series problems.
• Commodity trading exposure or an understanding of how maritime data informs commodity markets.
• Containerization experience using Docker and/or Kubernetes for reproducible, deployable pipelines.
• GeoPandas for geospatial data manipulation and analysis in Python.
• Shipping-domain knowledge, including vessel classes, charter markets, freight rates, and IMO regulations.