Ameresco

Ameresco

Energy Engineer

United States - Remote

Sponsorship not specified$81k-$129kDetected 30 days ago
PythonData AnalysisElectrical EngineeringCommunicationPublic Speaking

About the role

  • Ameresco has an immediate opening for a Energy Engineer / Analyst to join our Performance Engineering team, supporting a diverse multi-site portfolio spanning commercial office, K-12, healthcare, and government facilities.
  • The anticipated base salary range for this role is $80,900 - $128,750 (presented in good faith).
  • Actual pay will depend on factors such as internal equity, skills, experience, education, certifications, and location.

Responsibilities

  • Support the development and implementation of campus metering systems (electricity, gas, steam, chilled water)
  • develop specifications and single-line diagrams
  • Configure, deploy, and manage FDD rule sets within EMIS/analytics platforms
  • triage and prioritize fault alerts and develop fault impact estimates to support O&M decision-making.
  • Deliver clear technical reports and presentations translating complex analytics into actionable guidance for both technical and non-technical stakeholders.
  • Support the development and implementation of campus metering systems (electricity, gas, steam, chilled water); develop specifications and single-line diagrams; oversee meter commissioning and EMIS/DAS integration.
  • Configure, deploy, and manage FDD rule sets within EMIS/analytics platforms; triage and prioritize fault alerts and develop fault impact estimates to support O&M decision-making.
  • This role blends deep building systems expertise, data analytics fluency, and customer engagement - operating at the intersection of engineering, FDD/MBCx, and M&V.

Requirements

  • Bachelor's degree in Mechanical/Electrical Engineering, Energy Systems, Computer Science, or related STEM field.
  • Minimum of 3 years of experience in energy engineering, building performance analysis, or related discipline.
  • Travel Required.
  • Experience leveraging AI tools effectively and appropriately in an energy engineering context.
  • solutions-oriented mindset with ability to work independently in a fast-paced environment.

Nice to have

  • CEM, CMVP, or CCP certification.
  • Prior customer-facing or consulting experience.
  • Hands-on experience troubleshooting, retro-commissioning, or monitoring based commissioning of HVAC systems and central cooling and heating plants.
  • Familiar with measurement and verification methods and protocols.
  • Working knowledge of BAS/BMS platforms.
  • Energy and data analysis experience, including:
  • Data driven modeling,
  • Interval meter data analysis,

Compensation

  • We disclose salary ranges and benefits in all required external and internal postings and will provide further details upon request at any stage of the hiring process.
  • We are proud of our comprehensive and competitive employee benefits, including people-oriented insurance, investment, and incentive plans.
  • The anticipated base salary range for this role is $80,900 - $128,750 (presented in good faith).

Benefits

  • We are a trusted, full-service partner to public sector and government entities, K-12 schools, higher education, utilities, and healthcare customers across the U.S., Canada, the U.K., and Europe.

Company info

  • Ameresco, Inc. (NYSE:AMRC) is a leading energy solutions provider dedicated to helping customers reduce costs, enhance resilience, and decarbonize to net zero in the global energy transition.
  • At Ameresco, we show the way by developing, constructing and operating tailored smart energy efficiency solutions, distributed energy resources, and infrastructure upgrades that drive cost savings, resilience, decarbonization, and innovation.
  • Our comprehensive portfolio is built to address the challenges of today and adapt the future, ensuring long-term sustainability and success for our customers.

Equal opportunity

  • Ameresco is an Equal Opportunity Employer.

This listing is sourced directly from Ameresco's careers page and normalized into a canonical job model.