Reliability Engineer II

ASARCO

5h•Onsite

About The Position

Join ASARCO: Pioneering the Future of Copper Mining. At ASARCO, we are not just producing copper—we are shaping the future of mining. Founded in 1899, ASARCO stands as a cornerstone of Arizona's mining industry, with our Ray Mine being the first open-pit copper operation in the United States. Today, Ray Mine continues to lead the way in innovation, boasting a state-of-the-art concentrator equipped with cutting-edge machine learning models and advanced process control systems. We are also at the global forefront of autonomous technology: pit haulage at Ray Mine is now powered by a fully operational Autonomous Haulage System (AHS). By utilizing these self-driving trucks, we have significantly enhanced safety and operational efficiency while maintaining our commitment to our workforce. At ASARCO, we offer more than a job; we provide an opportunity to be part of a legacy of excellence and a future of technological advancement. Apply today and help us continue to lead the way in the mining industry. Position Summary: We are seeking a highly experienced and technically proficient Reliability Engineer to perform reliability engineering activities at a major copper mining operation in Arizona. This pivotal role is responsible for driving equipment reliability improvement initiatives across critical rotating assets, applying root cause analysis, FMEA and other reliability engineering tools and best practices. The ideal candidate brings deep technical expertise in mining equipment reliability, and the ability to align reliability strategies with production and maintenance goals.

Requirements

Understanding of the operation of mining equipment (crushers, mills, pumps, flotation, filters, haul trucks, shovels, drills).
Knowledge of probability and statistical analysis.
Ability to develop and maintain awareness of occupational hazards and safety precautions.
Ability to apply principles of logical or scientific thinking to a wide range of intellectual and practical problems.
Ability to communicate effectively, both orally and in writing in English.
Ability to read plans, electrical, and equipment drawings.
Ability to write routine reports and maintenance records.
Must have good computer skills with MS Windows, MS Excel, MS Outlook, and PowerPoint.
Minimum Education Required: Bachelor’s degree in a STEM discipline (Science, Technology, Engineering, and Math).
Minimum Experience Required: 5 years.
Effective written, verbal, and communication skills to satisfy training, safety, and electronic message transmission policies of the company.
Candidates must possess a current and valid driver’s license.

Nice To Haves

Knowledge of SAP, Power BI, and/or Machinery Health Manager a plus.

Responsibilities

Lead formal Root Cause Analysis for defects elimination and prevention of failures and implement permanent proactive engineering fixes.
Develop and maintain FMEA models to prioritize maintenance based on production risk and safety.
Support maintenance and production department on implementing improvement actions of production equipment reliability.
Review and refine PM/PdM tasks to ensure every maintenance hour is technically justified and adds value.
Bad Actor Elimination: Identify top recurring equipment failures using Pareto and statistical analysis, and develop proactive measures, and monitoring systems to prevent future failures.
Perform Life Cycle Costing (LCC) to guide equipment replacement and CAPEX budget decisions.
Apply Reliability Centered Maintenance principles to optimize the functional performance process equipment.
Monitor MTBF and MTTR trends to proactively identify and correct systemic reliability declines.
Partner with OEMs to improve component reliability for better performance in abrasive, high-tonnage mining environments.
Ensure reliability-based improvements are integrated into the weekly planning and scheduling workflow.
Data collection and analysis using predictive technologies: Vibration, Infrared thermography, Tribology, Ultrasonics, Dye Penetration Testing, Motion Amplification etc.
Provide training on reliability tools and best practices.
Leverage predictive analytics tools and machine learning models developed in the AVEVA system to proactively identify failure patterns, optimize maintenance strategies, and reduce equipment downtime.