Senior Systems Engineer – Predictive Fleet Intelligence

Responsibilities

• Develop the long range engineering plan for monitoring the health and performance of complex industrial assets, including control, power, mechanical systems, supporting infrastructure, communications networks, and deployed software configurations.
• Draft comprehensive monitoring specifications outlining which equipment to monitor, normal operating conditions, and indicators of degradation or impending failure.
• Set up system health logic, thresholds, operating limits, and classification criteria to separate healthy operation from abnormal states.
• Specify data acquisition needs by identifying signals, parameters, instrumentation, and operational data required for meaningful health assessment.
• Create engineering specifications for predictive monitoring capabilities, documenting leading indicators, failure signatures, and detection logic for early fault identification.
• Develop asset-class specific monitoring standards and predictive detection strategies across the fleet, defining how healthy operation, degradation, and impending failure appear in operational data.
• Identify leading indicators and detection criteria to enable early recognition of developing equipment failures for proactive intervention.
• Define monitoring strategies for software version compliance and control system configuration management to maintain fleet-wide consistency.
• Continuously refine alarm rationalization and monitoring approaches to deliver timely, actionable information while reducing nuisance alarms.
• Provide engineering specifications for Software Engineering and Data Science teams to build fleet-wide monitoring capabilities.
• Translate root cause investigations, reliability analyses, operational learning, and performance trends into predictive monitoring strategies to prevent recurrence of failures.
• Build and maintain a knowledge base of failure modes, leading indicators, detection thresholds, probable causes, assumptions, and recommended field responses.
• Analyze fleet performance, downtime, and events to identify recurring failure mechanisms and prioritize predictive monitoring opportunities.
• Develop time-series analysis approaches to characterize how degradation progresses across major equipment systems.
• Draft detection rule specifications for abnormal behavior and collaborate with Data Science to implement predictive analytics applications.
• Continuously expand predictive monitoring coverage by identifying new early-detection opportunities to improve reliability.
• Establish governance processes to ensure investigated root causes, lessons learned, and reliability improvements are embedded in monitoring standards, rules, and the knowledge base.
• Improve the Rules Library by validating detection logic against outcomes and incorporating new failure signatures fleet-wide.
• Define engineering specifications for predictive alerts that include context, trends, probable causes, recommended actions, and expected outcomes for informed decision making.
• Design alert thresholds and notification strategies to maximize actionable early warnings while reducing false positives and fatigue.
• Define escalation architectures to route information to the right personnel based on severity, risk, and business impact.
• Lead the deployment of new predictive monitoring capabilities with communication plans, response guidance, and training for Operations, Maintenance, and Technical Support teams.
• Develop and maintain engineering metrics to measure alert effectiveness, intervention success, and monitoring system performance.
• Conduct regular reviews of monitoring effectiveness and refine detection logic, thresholds, and alert strategies using operational feedback and results.
• Specify fleet health dashboards, operational bulletins, and decision-support products that provide structured visibility into condition, risks, and reliability trends.
• Define standardized fleet health reporting products including summaries, trend reports, and reliability analyses to support proactive maintenance and decision making.
• Design standardized alert response packages with technical context, historical information, probable causes, recommended first actions, and resolution criteria for field and remote teams.
• Partner with Digital Solutions to deliver fleet intelligence through enterprise dashboards, mobile apps, and other digital platforms.
• Establish engineering standards, governance, and best practices to enable scalable, repeatable fleet health monitoring across the organization.

Requirements

• Bachelor's degree in Mechanical, Electrical, Controls, Systems, Petroleum Engineering, or a related engineering discipline.
• Eight or more years of experience in systems engineering, reliability engineering, industrial controls, automation, predictive maintenance, asset performance management, or complex industrial systems.
• Experience with large scale industrial equipment in asset intensive sectors such as drilling, mining, heavy equipment, manufacturing, marine, rail, power generation, or similar environments.
• Strong understanding of industrial control systems, instrumentation, condition monitoring technologies, and operational data platforms.
• Experience working with industrial historians, SCADA systems, PLC based equipment, or time-series operational data.
• Working knowledge of root cause analysis, failure modes and effects analysis, reliability engineering, and predictive maintenance methodologies.
• Proven ability to develop engineering standards, technical specifications, system requirements, monitoring strategies, or equipment health frameworks.
• Experience collaborating with software engineering, digital product, or data science teams to deliver industrial monitoring or predictive analytics solutions.
• Experience designing predictive maintenance or condition monitoring programs for complex industrial assets.
• Familiarity with machine learning, industrial analytics, digital twins, or asset performance management platforms.
• Knowledge of industrial networking, automation platforms, fleet management systems, and equipment health monitoring technologies.
• Experience defining engineering requirements for digital products or enterprise monitoring systems.
• Strong technical writing, analytical reasoning, and systems thinking skills.
• Excellent communication and stakeholder management abilities with experience influencing cross-functional engineering teams.

Technologies

• SCADA systems
• Industrial historians

Additional Details

• This role requires the ability to operate in a time sensitive, high visibility environment and to travel regularly to support business units and on-site visits. Extended travel to remote locations and overnight stays may be required. Hours may include weekends, holidays, and travel within or outside the assigned region. The engineer should prioritize planning, multitasking, and prioritization in everyday work.