Potomac Fund Management ML Data Engineer

Required - Top 3:

1. Python & SQL – this is the technical foundation of everything
2. Data Pipeline & ETL/ELT experience
3. Modern Data Platform experience – Data lakes, Data Warehouse or Lakehouse Architecture

Specific Outcome This Position Will Produce: The specific outcome is to build a centralized, reliable, and AI-ready data infrastructure that ensures clean, automated, and trustworthy data flows across Potomac's systems to power their machine learning models and investment decisions.

Typical Day-to-Day:

• Building and maintaining data pipelines, pulling data in from APIs, trading platforms, SaaS tools, and databases automatically
• Cleaning and transforming data, making raw messy data reliable and usable through ETL/ELT processes
• Managing the data lake/lakehouse, maintaining the central repository where all company data lives
• Building ML-ready datasets, preparing clean structured data so machine learning models can run against it
• Monitoring data quality, setting up alerts and automated checks to catch data issues before they cause problems
• Collaborating with analytics and business teams, translating what the business needs into data solutions
• Supporting BI dashboards and reporting, making sure downstream tools like Power BI or Tableau have reliable data feeding them
• Improving the platform, continuously optimizing pipelines for speed, cost, and reliability