Maintenance Expert

We are searching for a GLOBAL MAINTENANCE EXPERT to support the predictive maintenance projects for a client in the pharmaceutical sector in the region of Brussels.

• Define, maintain and communicate the global Predictive Maintenance strategy, roadmap and governance model in alignment with Asset Management and Reliability objectives.
• Develop and own the Predictive Maintenance Playbook, covering use-case selection, asset eligibility, monitoring strategies, alarm governance, operating models and lifecycle requirements.
• Establish global technical standards and minimum requirements for condition-monitoring technologies, data acquisition, connectivity, analytics, diagnostics and integration with maintenance processes.
• Build a risk-based methodology to prioritize assets and select the appropriate predictive techniques based on asset criticality, failure modes, detectability, business impact and value potential.
• Provide expert guidance on vibration analysis, ultrasound, thermography, oil and lubricant analysis, motor and electrical condition monitoring, process-parameter analytics and other relevant predictive techniques.
• Assess AI, machine learning, Asset Performance Management, digital twin and advanced analytics solutions for technical relevance, scalability, explainability and sustainable business value.
• Lead technical evaluations, Proofs of Concept and qualification activities for predictive-maintenance technologies in collaboration with site, project, Quality, EHS, Digital Technology and Procurement stakeholders.
• Define functional and data requirements for predictive-maintenance capabilities in new assets and major capital projects through Reliability by Design and Asset Ready for Maintenance processes.
• Support the integration of predictive findings into CMMS/EAM workflows, work management, maintenance plans, RCM/FMECA outputs, asset strategies and continuous-improvement processes.
• Combine engineering expertise with historians, CMMS/EAM, APM, data landscapes, data warehouses, IoT solutions and analytics data to generate actionable asset-health insights.
• Perform or support complex diagnostic investigations and translate condition, process and reliability data into clear technical recommendations and risk-based actions.
• Develop business cases and value-tracking methodologies covering avoided failures, risk reduction, equipment availability, maintenance productivity, lifecycle cost and sustainable resource use.
• Define and monitor global predictive-maintenance KPIs and asset-health indicators and contribute to dashboards, management reviews and cross-site performance comparisons.
• Build and coordinate a global network of predictive-maintenance practitioners and external experts, sharing lessons learned, standards, reference architectures and proven practices.
• Develop competency matrices, training content and coaching approaches for Reliability, Maintenance and Engineering teams.
• Monitor external technology developments and pharmaceutical-industry practices and recommend innovations that strengthen reliability, compliance and lifecycle performance.
• Provide technical oversight of strategic suppliers, including competency expectations, deliverable quality, data ownership, service performance and standardization opportunities.
• Ensure that solutions and resulting decisions are documented in accordance with applicable cGxP, data-integrity, Quality, EHS, cybersecurity and internal procedural requirements.
• Spend approximately half of the time on hands-on technical work, including designing predictive-maintenance processes and defining interactions with current and future systems.
• Spend approximately half of the time on stakeholder engagement, reporting, presentations and communicating complex or highly technical ideas in a clear and accessible way.
• Create visibility and engagement around predictive maintenance, address potential resistance to new approaches and influence stakeholders without hierarchical authority.
• Work with a high level of autonomy, proactively identify and connect with the appropriate stakeholders and integrate rapidly into the organization.
• Focus on technical delivery and reporting rather than on financial project management.

• Minimum 8 years of relevant experience in reliability, maintenance engineering, condition monitoring, asset management or industrial analytics.
• Candidates with 6-8 years of experience may be considered only if they already demonstrate strong, directly applicable expertise and can operate independently without requiring technical upskilling in predictive maintenance.
• Candidates with significantly more experience may also be considered, provided their expertise remains hands-on, technically relevant and aligned with the data, systems and technology dimensions of predictive maintenance.
• Demonstrated experience deploying predictive-maintenance solutions in a multi-site or regulated industrial environment is required.

• Strong hands-on expertise in predictive maintenance and condition-based maintenance. The candidate must have directly designed, implemented or operated these solutions rather than only having worked around the topic.
• Advanced knowledge of reliability engineering, failure mechanisms, asset criticality, FMECA, RCM, RCA and maintenance-strategy optimization.
• Strong understanding of predictive-maintenance systems and their underlying technology, including how solutions function, are configured or coded and connect to the broader data landscape.
• Practical experience with data warehouses, IoT technologies, sensor technologies, data acquisition, signal interpretation, diagnostic reasoning and asset-health analytics.
• Ability to integrate engineering knowledge with historians, CMMS/EAM, APM and analytics data to produce actionable technical insights.
• Knowledge of AI-enabled predictive-maintenance tools, industrial analytics or comparable advanced digital solutions.
• Experience defining or implementing scalable technical standards, system requirements, governance models, roadmaps and operating processes.
• Practical understanding of data quality, contextualization, governance, system integration, cybersecurity and data-integrity considerations.
• Knowledge of cGMP, Good Engineering Practices, EHS requirements and risk-based approaches within regulated environments.
• Experience in the pharmaceutical, biotechnology or a closely comparable regulated industrial environment. Petrochemical experience may be considered when the candidate demonstrates highly relevant predictive-maintenance expertise and a strong understanding of regulated ways of working.
• Ability to translate business and asset risks into technical requirements, implementation roadmaps and quantified value cases.
• Strong analytical and structured problem-solving capabilities, sound judgment under uncertainty and a pragmatic deployment mindset.
• Ability to work independently and deliver expertise that is not currently available internally.
• Strong stakeholder-management and influencing skills, including the ability to coordinate international, cross-functional and multicultural stakeholders without hierarchical authority.
• Excellent communication and presentation skills, with the ability to adapt communication from field technicians and engineers to senior management without losing technical rigor.
• Ability to simplify complex technical concepts, build engagement and address resistance to new maintenance approaches.
• Excellent command of English.
• Master’s degree in Mechanical, Electrical, Industrial, Automation, Data or Reliability Engineering, or an equivalent qualification.
• Willingness to travel internationally when required for technology evaluations, projects and site deployments.

• Experience in maintenance-organization design or broader maintenance-service models.
• French or another language used across the organization’s sites.
• Experience coaching practitioners, developing communities of practice and building sustainable organizational capabilities.
• Experience with Reliability by Design and Asset Ready for Maintenance approaches in capital projects.
• Relevant certifications such as CMRP, CRL, vibration analysis ISO 18436, thermography, ultrasound, lubrication analysis, IAM Certificate or Diploma, Lean Six Sigma or data analytics.
• Exposure to digital twins, advanced Asset Performance Management solutions and machine-learning applications.
• Experience supervising strategic technology suppliers and defining expectations regarding competencies, deliverables, data ownership and service quality.

• Hybrid working model, minimum 3 days/week on site.
• Full-time availability is preferred, although a four-day-per-week arrangement may be considered.

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