Lead Fleet Reliability Data Engineer

Job Description SummaryWe are seeking a Fleet Reliability Data Engineer to join the Fleet Performance & Analytics team within the Fleet Intelligence & Reliability organization. This role will build and maintain the trusted data foundation required to understand the health, performance, reliability, and intervention history of GE Vernova's global Solar and Storage installed fleet.

The engineer will connect operational telemetry, alarms and events, asset hierarchy, equipment configuration, software versions, maintenance activities, component replacements, field interventions, failure records, and Root Cause Analysis findings into reliable and scalable engineering datasets. The role will ensure that fleet data is complete, contextualized, traceable, and accessible for reliability analysis, performance monitoring, technical investigations, and predictive analytics.

This role is distinct from a traditional enterprise data-engineering position. It requires strong data-engineering capability combined with an understanding of industrial assets, reliability concepts, and engineering workflows. The successful candidate will partner closely with Reliability & RCA, Data Analytics & AI, Product Engineering, Controls, Digital Technology, Quality, and Field Operations to convert fragmented fleet information into durable engineering intelligence.Job DescriptionRoles and Responsibilities

• Design, build, and maintain scalable data pipelines that ingest and integrate operational telemetry, alarms, events, maintenance records, field interventions, asset configuration, software versions, and engineering findings.
• Develop and maintain a standardized fleet asset model and hierarchy covering sites, systems, equipment, assemblies, components, serial numbers, configurations, and relevant parent-child relationships.
• Establish traceability for significant interventions, component replacements, repairs, configuration changes, software updates, and other lifecycle events affecting critical fleet equipment.
• Create curated and reusable reliability datasets that support Root Cause Analysis, failure trending, recurrence analysis, fleet exposure assessment, performance monitoring, and corrective-action validation.
• Develop robust methods to link operational events and alarms with maintenance actions, failure records, product configuration, environmental conditions, and investigation outcomes.
• Define and implement data-quality rules for completeness, accuracy, consistency, timeliness, uniqueness, lineage, and contextual integrity.
• Build automated controls that identify missing data, inconsistent asset identifiers, invalid timestamps, duplicate interventions, configuration conflicts, and broken data relationships.
• Partner with Reliability & RCA engineers to structure investigation data, identify comparable fleet events, define affected populations, and preserve reusable evidence from completed RCAs.
• Partner with Data Analytics & AI engineers to provide governed, documented, and analysis-ready data products for dashboards, anomaly detection, predictive models, and engineering decision-support tools.
• Develop fleet master-data standards, naming conventions, taxonomies, failure classifications, intervention categories, and metadata required for consistent fleet-level analysis.
• Integrate data from industrial historians, SCADA systems, remote-monitoring platforms, service-management systems, engineering databases, and other relevant sources.
• Create reliable APIs, data services, semantic layers, and governed access patterns that enable engineering teams to use fleet data efficiently and consistently.
• Maintain data lineage, source-to-target mappings, interface specifications, transformation logic, ownership definitions, and technical documentation for reliability data products.
• Implement monitoring and alerting for data-pipeline health, ingestion failures, schema changes, latency, processing errors, and data-quality degradation.
• Support migration and harmonization of historical fleet data while preserving source context, auditability, and engineering meaning.
• Work with cybersecurity, data-governance, and platform teams to ensure appropriate access control, retention, privacy, backup, recovery, and lifecycle management.
• Improve engineering productivity by automating repetitive data preparation, reconciliation, event correlation, fleet-population analysis, and reliability reporting activities.
• Communicate data limitations, quality risks, dependencies, and remediation priorities clearly to engineering and leadership stakeholders.
• Promote a culture of data ownership, traceability, technical rigor, collaboration, and continuous improvement across the Fleet Intelligence & Reliability organization.

Required Qualifications