Potomac Fund Management ML Data Engineer
Job Description
Job Description Required - Top 3:
- Python & SQL – this is the technical foundation of everything
- Data Pipeline & ETL/ELT experience
- 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