Senior Engineer, Aeroderivative Performance & Operability - Field Test Owner

About the position The Aeroderivative Performance & Operability Engineering team is a critical pillar within the Aeroderivative Engineering organization, operating at the intersection of engineering, commercial, and digital domains. The team covers project execution and model development for aspects related to performance (thermodynamic cycle models) and operability (including emissions, fuel flexibility, and controls). We are seeking a seasoned aeroderivative gas turbine expert who bridges world-class field testing with advanced cycle modeling. This role demands someone who can both troubleshoot units on-site and drive the quality of data that feeds our NPSS models-someone who understands that field test rigor directly impacts model accuracy. You will be the technical anchor for our OTR field activities, transforming how we collect, validate, and leverage field data to create increasingly precise steady-state and transient cycle models. This is a hands-on leadership role for an engineer who has mastered both the physics of aeroderivative gas turbines and the discipline of field testing. You will establish the standards for how we prepare for, execute, and extract value from every test. As a senior member of the team, you will be instrumental in training our early-career engineers, establishing best practices, and ensuring we deliver with the highest level of technical rigor on every project.

Responsibilities

• Establish Rigorous Testing Standards & Protocols: Develop comprehensive procedures, calculation tools, and pretest readiness protocols that ensure data quality suitable for advanced cycle model validation. This includes instrument checkouts, sensor calibration verification, conditioning runs, and defining the critical data collection parameters needed for data reduction and model matching.
• Lead Technical Test Preparation & Execution: Drive quality improvements into the test procedure, instrumentation plans, and pretest readiness process in commissioning to ensure data quality improves for cycle models, unit variation decreases, and execution quality is high. Serve as lead for field testing, performing real-time data quality assessment and troubleshooting performance issues using both field diagnostics and cycle model analysis.
• Drive Data Quality for Cycle Model Development: Work with the cycle modeling team to ensure field data packages support NPSS steady-state and transient model validation. Execute data reduction and preliminary model matching in the field, applying gas turbine thermodynamic principles to interpret performance deviations and guide data collection strategies.
• Mentor and Develop Team Capabilities: Take primary responsibility for developing junior engineers in both field procedures and underlying physics, teaching the connection between rigorous field data collection and accurate cycle modeling.
• Coordinate & Improve: Serve as technical point of contact for third-party vendors, analyze past test results to identify improvements, and drive consistency in data collection and validation across all field operations.

Requirements

• This role requires significant experience in the Engineering/Technology & Performance Engineering.
• Knowledge level is comparable to a Master's degree from an accredited university or college ( or a high school diploma with relevant experience).
• Willingness and ability to travel up to 50% of the time.
• Relocation to work in a hybrid model in Greenville, SC.

Nice-to-haves

• Deep understanding of Aeroderivative gas turbine performance principles, data acquisition systems, and instrumentation.
• Expertise in industry-standard performance test codes (e.g., ASME PTC 22, PTC 46).
• A proven track record of writing technical procedures and developing engineering calculation tools from scratch.
• Demonstrated experience in a formal or informal mentorship role, with a passion for teaching others.
• Excellent communication and customer-facing skills, with the ability to clearly explain complex technical issues.
• Strong analytical skills and proficiency with data analysis tools (NPSS, Python, etc.).

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