Renewable Asset Maintenance & Analytics Lead

EDP Renewables APAC

Santo Niño 1st

On-site

PHP 600,000 - 1,000,000

Full time

14 days+

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Benefits offered by this job

Flexible work conditions

Career development opportunities

Cross-cutting projects and mobility

Job summary

EDP APAC is seeking a Maintenance Programme Analyst to independently develop and prioritise maintenance programmes for the solar asset portfolio. You will convert maintenance requirements into optimized work-order packages, govern backlog, validate data quality and apply AI-assisted models to improve asset prioritisation and resource planning.

You will also develop dashboards on maintenance delivery, backlog and reliability, and collaborate with technical engineers to refine maintenance

Qualifications

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.
Experience developing, reviewing or improving preventive, predictive and corrective maintenance programmes.
Experience analysing maintenance histories, failure patterns, equipment criticality, backlog and asset‑performance information.
Proficiency in maintenance planning, scheduling, backlog management and work‑readiness assessment.

Responsibilities

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.
Develop and maintain a transparent prioritisation model that considers safety, asset availability, equipment criticality and other factors.
Generate prioritised work-order packages for each cluster.
Identify critical, urgent, routine and deferrable activities.
Record and explain any manual changes to model-generated priorities.
Rebalance the programme as conditions change.
Maintain accurate view of preventive and corrective maintenance backlog.
Analyse backlog by cluster, asset type, risk, age and impact.
Identify overdue or misclassified work orders and facilitate backlog reviews.
Escalate high-risk or long-outstanding work to Head of Asset Operations.
Model resources and time to recover backlog.
Verify completed work orders have readings, photos, labour hours and materials.
Return incomplete submissions for correction.
Maintain traceability from trigger to closure.
Develop dashboards for maintenance delivery, backlog, response time and reliability.
Apply AI-enabled modelling to forecast workload and improve prioritisation.
Test model outputs against actual results before wider implementation.
Maintain human oversight for high-risk decisions.
Refine prioritisation logic using historical performance and feedback.
Report planned vs completed maintenance and explain variances.
Forecast backlog, capacity and risks.
Collaborate with Technical Engineers to convert findings into revised tasks.