About the job
Join the Market Leader in Electric Power Data and Analytics Solutions
The electrical grid is the largest and most intricate machine ever constructed. Yes Energy’s cutting-edge electric power trading analytics software offers real-time insight into the vast amounts of data generated by the North American electrical grid daily. Our distinct and innovative perspective on this data empowers real-time trading decisions and informs mid-to-long-term investments that help maintain low utility prices, support the energy transition, and ensure the grid's reliability. This role is not only challenging but also purposeful.
Be a part of our thriving, expanding business amidst global transformation.
Position Summary
As a seasoned Data Quality Engineer, you will be entrusted with the unwavering integrity of our core power infrastructure data. Your role will not merely involve data movement; you will design automated systems, complex SQL validators, and PL/SQL packages to ensure our data is comprehensive, timely, and exceptionally accurate. We are in search of a robust Data Quality specialist who blends extensive database programming skills with a meticulous QA approach.
Position Details
- Salary range: $90,000 - $115,000, depending on experience
- Location: Boulder, CO (Hybrid)
- Full-Time
- Reporting to: Director of Content Engineering
- Authorization: Applicants must be authorized to work in the US without visa sponsorship.
Primary Responsibilities:
- Advanced Data Quality Engineering: Develop and implement automated QA frameworks within the database to identify anomalies, data drifts, and logical errors in real-time.
- PL/SQL & SQL Development: Create complex, high-performance PL/SQL stored procedures, functions, and packages specifically for data validation, cleansing, and ETL audit trails.
- Deep-Dive Root Cause Analysis: Serve as the ultimate safeguard against data discrepancies. Utilize expert-level SQL and front-end tools to trace data issues back to source systems, primary research, user error, or transformation logic.
- Database Architecture for Quality: Collaborate on schema design to ensure data models enforce referential integrity and facilitate effective quality-control indexing.
- Automated Monitoring: Develop and maintain proactive "health check" dashboards and alert systems that oversee the completeness and timeliness of thousands of daily data feeds.
- Process Standards: Lead the charge in establishing and upholding process standards that bolster data integrity.

