Madison Energy Infrastructure
Performance Engineer
About this role
We are looking for a Performance Engineer to optimize the performance of our extensive portfolio of solar and battery energy storage systems. You will analyze production data and identify underperformance, while also integrating new sites into our analytics framework and collaborating closely with asset management teams.
What you'll do
- Analyze production data across 600+ solar and BESS sites.
- Investigate root causes of underperformance in asset operations.
- Flag underperformance issues to Regional Asset Managers.
- Contribute to monthly production reporting and variance analysis.
- Digitally onboard new sites into the analytics system.
- Validate pro-forma energy models and as-built drawings.
What they're looking for
- 5+ years of solar monitoring experience
- Expertise in DAS/SCADA platforms
- Understanding of performance metrics
- Data analysis and reporting skills
- Root-cause analysis capabilities
- Knowledge of energy modeling
- Experience with Power BI
- Proficiency in interpreting weather data
Benefits
- Remote work opportunity
- Work with a rapidly growing solar company
- Collaborative team environment
- Focus on safety and performance
- Impactful role in renewable energy
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Madison Energy Infrastructure
Madison Energy Infrastructure develops and operates solar and battery energy storage systems, managing projects from development through construction and ongoing optimization. The company is hiring Project Engineers to oversee solar project execution and Performance Engineers to optimize system performance and analytics across their portfolio.
View all jobs at Madison Energy InfrastructureLikely interview questions
- Walk us through how you've diagnosed a site underperforming at 85% Performance Ratio. What was your investigation process, and how did you distinguish between equipment failure, soiling, weather anomalies, and data quality issues?
- Tell us about your hands-on experience with AlsoEnergy (or Power Factors) and SCADA platforms. How have you used sub-inverter and string-level data to catch issues that site-level metrics missed?