Job Description:


Responsible for developing and maintaining life cycle and downtime data models to support the
reliability and performance of mining assets. This role focuses on creating predictive maintenance strategies, analysing lifecycle costs, developing asset life plans and managing annual master operating schedule. The role plays a critical part in ensuring the reliability and efficiency of equipment, with a focus on cost control and service strategy management.


Data Analysis and Modelling:


Maintain PowerBI data models for reliability analysis, focusing on predicting equipment performance and identifying potential failures.


Service Strategy and Scheduling:


Maintain life cycle and defect elimination data and support the closure of any identified defects.


Reporting and Compliance:


Prepare detailed reports on equipment performance, service records, and outstanding defects elimination action for senior management and other stakeholders.


Continuous Improvement:


Participate in continuous improvement initiatives, contributing to the development of more efficient maintenance strategies.
Work closely with the Maintenance team to implement data-driven improvements and optimize equipment performance.


Qualifications:

Formal qualifications


NQF6 National Diploma or Bachelor’s degree in Data Science or Engineering.
Anglo Planner qualification (advantageous)


Experience


3-5 years of experience in reliability engineering, data analysis, engineering, or maintenance planning.
Working understanding of data analysis tools such as PowerBI, SAP PM, and Microsoft Office Suit
Underground trackless mobile experience or Processing plant experience


Additional requirements


Working understanding of PowerBI and SAP PM: Basic skills in data modelling, visualization, and analysis using PowerBI and SAP PM
Knowledge of reliability and maintenance principles: Familiarity with reliability-centred maintenance (RCM) practices and predictive maintenance.
Knowledge of Root Cause Analysis (RCA), failure mode and performing predictive maintenance analysis.