Asset Operations & Maintenance Supervisor

EDP APAC is the main subsidiary of EDP Group, a global leader in the renewable energy sector and one of the world’s largest wind producers. With its headquarters in Singapore, EDP APAC is the leading sustainable development hub for the Asia Pacific region with activities across nine different markets. Our activities are focused on the design, development, management, and operation of renewable energy sources, namely solar, wind, as well as new technologies such as storage.

EDP is a global energy group leading the Energy Transition, Innovation and Sustainability. Using the technology of the future, we create solutions highly focused on the needs of our people and our customers, never neglecting our role and contributions to society. To achieve our goals, we aim to attract diverse people with high potential through the professional opportunities we create.

Join us to be part of a renewable energy leader that reinvests in society through sustainable projects and social as well as cultural causes. You will have the opportunity to actively participate in our global transformation, by changing tomorrow now.

 Summary:

The Maintenance Programme Analyst independently develops, prioritises and controls the portfolio’s maintenance programme. The role converts preventive maintenance requirements and corrective maintenance triggers into optimised work-order programmes for execution by the clusters.

The position also governs maintenance backlog, validates completion data and applies analytics and AI-assisted models to improve prioritisation, resource planning and maintenance performance. 

Responsibilities: 

Maintenance Programme Development

• Develop annual, quarterly, monthly and weekly maintenance programmes for the solar asset portfolio. 
• Establish preventive maintenance activities and frequencies based on equipment requirements, contracts, regulations, asset criticality and operating experience. 
• Consolidate corrective maintenance triggers received from the Monitoring team, Asset Management, Technical Engineers and Cluster Leads. 
• Include shutdown windows, site-access restrictions, statutory deadlines and known resource constraints in the programme. 
• Maintain a forward forecast of workload, manpower, contractor and material requirements. 

Prioritisation and Work-Order Planning

• Develop and maintain a transparent prioritisation model that considers: 
  o    Safety and regulatory risk 
  o    Asset availability and generation loss 
  o    Equipment criticality 
  o    Contractual obligations 
  o    Fault severity and duration 
  o    Work-order age 
  o    Risk of further failure 
  o    Resource and spare availability 
  o    Geographic efficiency 
  o    Management direction
• Generate prioritised work-order packages for each cluster. 
• Identify critical, urgent, routine and deferrable activities. 
• Record and explain any manual changes made to model-generated priorities. 
• Rebalance the programme when operational conditions or management priorities change. 

Backlog Governance

• Maintain an accurate view of preventive and corrective maintenance backlog. 
• Analyse backlog by cluster, asset type, risk, age, fault category and business impact. 
• Identify overdue, repeatedly deferred, duplicated or incorrectly classified work orders. 
• Facilitate recurring backlog reviews with Cluster Leads. 
• Escalate high-risk or long-outstanding work to the Head of Asset Operations. 
• Model the resources and time required to recover excessive backlog

Completion Validation and Data Quality

• Verify that completed work orders contain the required readings, photographs, findings, labour hours and material records. 
• Return incomplete submissions to the relevant cluster for correction. 
• Ensure defects discovered during maintenance have corresponding follow-up actions. 
• Maintain consistent work-order classifications, failure codes and closure standards. 
• Preserve traceability from the original maintenance trigger through execution and final closure. 

Analytics and AI-Enabled Planning

• Develop dashboards covering maintenance delivery, backlog, response time, resource demand and asset reliability. 
• Apply AI-assisted modelling to identify failure patterns, forecast workload and improve work prioritisation. 
• Test model outputs against actual operational results before wider implementation. 
• Maintain human oversight for high-risk, safety-critical and commercially significant decisions. 
• Refine prioritisation logic using historical performance and feedback from execution teams.

Management Reporting and Programme Improvement

• Report planned versus completed maintenance and explain material variances. 
• Provide management with forecasts of backlog, capacity requirements and emerging operational risks. 
• Work with Technical Engineers to convert technical findings and incident lessons into revised maintenance tasks. 
• Review task frequencies and recommend risk-based changes to the maintenance strategy. 
• Maintain auditable records of programme assumptions, priority rules and approved changes.

Requirements: 

• Bachelor’s degree in Engineering, Data Analytics or another relevant engineering discipline.
• Typically 4–8 years of experience in maintenance engineering, operations, maintenance planning or asset management within the power, utilities, renewable-energy or comparable asset-intensive sector.
• Demonstrated experience developing, reviewing or improving preventive, predictive and corrective maintenance programmes.
• Experience analysing maintenance histories, failure patterns, equipment criticality, backlog and asset-performance information.
• Practical experience with computerised maintenance-management or enterprise asset-management systems, including maintenance plans, job plans, work orders and asset master data.
• Experience coordinating maintenance requirements with engineering, operations, contractors, OEMs and procurement teams.
• Experience with digitalisation, automated reporting or AI-enabled maintenance applications would be advantageous.
• Strong knowledge of preventive, predictive, condition-based and corrective maintenance principles.
•  Ability to develop risk-based maintenance programmes using equipment criticality, asset condition and engineering requirements.
• Proficiency in maintenance planning, scheduling, backlog management and work-readiness assessment.
• Advanced working knowledge of maintenance-management systems, asset master data and maintenance records.
• Strong analytical capability to identify failure trends, evaluate programme effectiveness and recommend maintenance improvements.