Sr. Failure/Risk Analysis Engineer – Defect Analyst
Job Summary
Job Description
As a Sr. Failure/Risk Analysis Engineer, you will analyze data to develop predictive failure algorithms and determine long-term product performance. You'll document root cause findings, drive corrective actions, design tests, and perform simulations for MTTF/MTBF calculations. This role involves contributing to the 8D process, supporting DFMEA, managing failure analysis, identifying improvements, and studying failures remotely. You will also work with Customer Service to ensure quick return and delivery of defective field units for diagnostics. The role requires you to work in a diverse, multi-cultural environment as part of a global team. In essence, this position is responsible for in-depth analysis to improve product reliability and performance.
Must have:
- Technical degree in relevant engineering field.
- Experience in data analysis and reliability engineering.
- Ability to produce technical documentation and presentations.
- Proven problem-solving skills with attention to detail.
- Good communication and teamwork skills.
Good to have:
- Experience with power supplies, inverters, or solar power.
- Knowledge of safe electrical operations for batteries.
- Experience with AI and Machine Learning for failure prediction.
- Certifications/accreditations in electrical or safety.
4 skills required
Job Details
Enphase Energy is a global energy technology company and a leading provider of solar, battery, and electric vehicle charging products. Founded in 2006, our innovative microinverter technology revolutionized solar power, making it a safer, more reliable, and scalable energy source. Today, the Enphase Energy System enables users to make, use, save, and sell their own power. Enphase is also one of the most successful and innovative clean energy companies in the world, with more than 80 million products shipped across 160 countries.
Join our dynamic teams designing and developing next-gen energy technologies and help drive a sustainable future!
What you will be doing:
- Analyze Fleet and manufacturing data to develop predictive failure algorithms to determine long term product performance.
- Document and communicate root cause findings and drive containment/corrective actions.
- Design and execute tests to de-risk new parts.
- Simulations of performance of components/materials to achieve accurate MTTF/MTBF calculations.
- Contribute to the 8D process and represent the Failure Analysis Team as part of the Failure Review Board.
- Support cross functional teams to perform DFMEA in early life cycle phase and recommend design changes.
- Determine the analysis required and manage failure analysis from external vendors as needed.
- Identify FA cycle time improvements and new failure modes.
- Collect data from our online diagnostic systems to study failures remotely and contribute to the development of predictive fleet analysis and remote recovery.
- Respecting and contributing to the development of safety rules.
- Work with Customer Service to ensure quick return and delivery of defective field units for diagnostics.
- Work in a diverse, multi-cultural environment as part of a global team.
Who you are and what you bring:
- A technical degree in Electrical/Mechanical Engineering, Power Electronics, Power Systems, Manufacturing, or equivalent and demonstrable experience and expertise in data analysis and reliability engineering calculations.
- Able to produce effective technical/process documentation, presentations, and schematics.
- Experience with power supplies, inverters, or solar power would be a plus.
- Proven ability to apply scientific problem-solving techniques with strong attention to detail.
- Ability to participate in a variety of cross-functional teams, a good team player.
- Excellent written, verbal, and technical communication skills.
- Knowledge of safe electrical operations for batteries, microinverters and gateways is a plus. Certifications/accreditations in electrical or safety are highly desired.
- Experience with AI and Machine Learning to predict failure modes would be desirable.
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