BESS Performance Engineer - 21004

BESS Performance Engineer

Employment Type Full-Time Priority Location Remote (Latin America, India, Pakistan) Working Hours Monday - Friday, 8:00 AM - 5:00 PM EST Target Start Date ASAP (Urgent Hiring) Compensation Final offer based on experience, skills, and interview performance

About The Company

Our client is a mission-driven renewable energy developer, owner, and operator specializing in distributed and community-scale solar and energy storage assets across the United States. The organization focuses on sustainable land use and environmental restoration while deploying clean power infrastructure to serve local communities.

About The Role

This position sits within the Asset Management team, which oversees a expanding portfolio of distributed generation energy assets. Reporting to the VP of Engineering, the BESS Performance Engineer will lead performance analysis, monitoring, and reporting for standalone Battery Energy Storage Systems (BESS) as well as AC- and DC-coupled PV + BESS assets.

Working closely with Data Science and Asset Management teams, you will establish BESS-specific key performance indicators, optimize operational and commercial efficiency, and collaborate with operations and maintenance teams, engineers, and project managers to extract data-driven operational insights.

Key Responsibilities

Performance Analysis & KPI Tracking

• Lead the monitoring, performance analysis, and operational reporting for a portfolio of distributed generation standalone BESS and paired solar + storage assets.
• Establish, calculate, and track critical BESS KPIs, including Round-Trip Efficiency (RTE), State of Health (SOH), auxiliary power consumption, availability, and State of Charge (SOC) management.
• Conduct complex degradation modeling and build predictive analytics to forecast battery life, track SOH trajectories against capacity guarantees, and optimize charging and discharging cycles.
  
  

Telemetry, Data Infrastructure & AI Automation

• Extract, clean, and analyze large-scale SCADA and telemetry datasets to troubleshoot underperforming systems and diagnose component failures.
• Partner with Data Scientists to integrate BESS telemetry with enterprise platforms, structure data storage, and automate internal and external reporting pipelines.
• Leverage Generative AI tools and LLMs to accelerate code development, automate data processing workflows, and streamline complex analytics.
  
  

Cross-Functional Collaboration & Reporting

• Model and evaluate performance impacts during transitions between standalone BESS and AC/DC-coupled BESS + PV configurations.
• Present operational findings, performance metrics, and recommended corrective actions or dispatch strategies to both technical and non-technical stakeholders.
  
  

Required

Qualifications & Skills

• Experience: 3-5 years of experience in performance engineering, battery modeling, or data analytics specifically focused on energy storage systems.
• Education: Bachelor's or Master's degree in Electrical Engineering, Computer Science, Data Science, Mathematics, Physics, or a related field.
• Technical Expertise: Solid understanding of battery energy storage architecture, degradation mechanisms, and performance modeling, along with experience analyzing AC and DC electric power systems.
• Programming & Analytics: Strong proficiency in Python, R, or SQL for time-series data manipulation and modeling.
• Modern Tooling: Hands-on experience with AI development tools (e.g., Claude Code, LLMs) to accelerate workflow automation and code development.
• Business Intelligence: Proficiency with analytical libraries and tools (e.g., Pandas, NumPy, Matplotlib, Power BI, Tableau).
• Data Sources: Proven track record working with SCADA, Data Acquisition Systems (DAS), and discrete event data for plant performance assessments.
  
  

Preferred

• Direct experience in renewable energy evaluating distributed generation or utility-scale BESS and PV assets.
• Experience with financial analysis in wholesale energy markets, ISO/RTO dispatch strategies, and Locational Marginal Pricing (LMP) optimization.
• Familiarity with industry SCADA/DAS platforms (e.g., AlsoEnergy, Powerfactors, Ignition, PI System, etc.).
• Practical experience with cloud platforms (AWS, Azure), data infrastructure, or Machine Learning algorithms for predictive maintenance.
• Familiarity with Computerized Maintenance Management Systems (CMMS) such as Fiix, Maximo, or Sitetracker.