Data Engineering Leader, Supply Chain Digital Automation

GE Vernova • Greenville, NC

1d • Onsite

About The Position

Build and scale the industrial data platform that powers real-time factory intelligence, AI-enabled operations, and digital manufacturing across the global manufacturing network. Implement and operate the data pipelines and platforms securely, ensuring technical integrity and lineage.

Requirements

• 10+ years of experience in data engineering, data platform architecture, or industrial data systems.
• Proven experience designing and operating real-time data pipelines for operational environments such as manufacturing, industrial IoT, utilities, or energy systems.
• Demonstrated expertise in streaming data architectures supporting high-frequency telemetry and event-driven workloads.
• Experience integrating data across industrial systems such as PLCs, historians, MES and ERP platforms, including familiarity with industrial connectivity protocols (OPC-UA, MQTT, Modbus, or similar).
• Deep expertise in designing and operating scalable data platforms in cloud or hybrid environments.
• Hands on experience implementing DataOps practices including CI/CD for data pipelines, automated testing, data quality monitoring and pipeline observability.
• Experience designing data models and contextualization frameworks for operational or industrial environments, including familiarity with ISA-95, asset hierarchies, or similar operational data structures.
• Experience implementing data governance frameworks, including lineage, access management, and auditability for operational data platforms.
• Experience working at the intersection of Information Technology (IT) and Operational Technology (OT) environments.

Nice To Haves

• Demonstrates strong systems thinking, with the ability to design industrial data architectures that support complex manufacturing ecosystems spanning machines, operational systems, and advanced analytics platforms.
• Curiosity and enthusiasm for understanding manufacturing processes and translating operational realities into scalable data engineering solutions.
• Strong operational mindset with a focus on data reliability, observability, and resilience in mission-critical environments.
• Ability to balance architectural rigor with pragmatic execution, enabling rapid delivery of data capabilities while maintaining platform scalability and long-term maintainability.
• Strong ability to collaborate with cross functional teams across engineering, manufacturing operations, and enterprise technology teams.
• Strong product mindset for data platforms, with the ability to translate operational needs into reusable data products with clearly defined consumers and service levels.
• Experience building data platforms that support AI, advanced analytics, or real-time operational decision systems in industrial environments.

Responsibilities

• Define and evolve the end-to-end industrial data platform architecture, spanning edge ingestion, plant-level data infrastructure, and enterprise-scale data platforms.
• Design event-driven data architectures capable of handling high-frequency machine telemetry, transactional MES data, and engineering datasets while supporting both real-time and historical analytics use cases.
• Lead the development of standard industrial data models and ontologies that contextualize raw equipment signals with asset hierarchy, process context, and production events.
• Establish standardized contextualization patterns across plants to enable consistent interpretation of machine, process, and quality data.
• Define standards for industrial edge data ingestion, including connectivity to PLCs, SCADA, historians, and industrial IoT gateways.
• Establish resilient ingestion patterns supporting high-frequency telemetry, buffering, and store-and-forward mechanisms for operational reliability.
• Build pipelines capable of processing high-frequency industrial telemetry at scale, ensuring low latency for operational decision-making use cases.
• Build the data infrastructure required for AI and advanced analytics, including curated feature datasets, training data pipelines, and reproducible data environments.
• Partner with AI/ML teams to ensure data pipelines support model development, training, validation, and operational deployment.
• Define and implement a comprehensive data observability framework that monitors the health, completeness, and reliability of industrial data across the full lifecycle - from equipment signal capture through ingestion, contextualization, storage, and application consumption.
• Establish standard observability metrics (latency, freshness, signal loss, data incompleteness), alerts, and operational playbooks to ensure rapid detection and automated resolution of data integrity issues impacting manufacturing operations.
• Maintain complete lineage visibility from source equipment signals through data pipelines and transformations to consuming applications, ensuring traceability of data dependencies and rapid root-cause analysis during data incidents.
• Monitor schema changes and contextualization mappings across industrial data pipelines to detect drift that may impact downstream analytics, AI models, or operational applications.
• Drive the implementation of DevOps principles for data pipelines (automated testing, version control, and rapid, error-free deployment of new or updated data products).
• Implement rigorous automated data quality framework and monitoring, at the point of origin and throughout the pipeline, to ensure the reliability and accuracy of sensor, machine, and production data.
• Integrate industrial data observability signals with enterprise observability platforms to provide unified visibility across applications, infrastructure, and data pipelines supporting smart manufacturing systems.
• Design deployment patterns that allow industrial data infrastructure to scale consistently across multiple factories while accommodating plant-level variations in equipment and processes.
• Establish repeatable “factory onboarding playbooks” for new sites joining the data platform.
• Enforce a consistent data model across all plants and equipment types (e.g., using standards like ISA-95) to ensure data consistency and usability regardless of the factory or machine brand.
• Collaborate with the CISO and CTO to design secure data movement patterns between OT networks, edge infrastructure, and enterprise platforms, ensuring compliance with industrial cybersecurity frameworks.
• Enforce governance that balances the speed and automation of DataOps with the cyber security demands of Operational Technology (OT), including data lineage auditability and version control.
• Establish a data product lifecycle strategy, including ownership, SLAs, versioning, consumer documentation, and lifecycle management.
• Define standards for discoverability, reuse, and governance of industrial data assets.
• Establish formal data contracts governing the exchange of data between industrial systems including MES, ERP, quality systems, and AI applications to ensure stable integrations and predictable data behavior.
• Collaborate with the OT product leader, Global Process Digital Authority and Global Process Engineering Authority to ensure data solutions meet business needs and technical requirements.
• Recruit, mentor, and grow a team of data engineers specializing in industrial telemetry, streaming architectures, reliability engineering and manufacturing data systems.
• Establish engineering standards, career paths, and technical practices aligned with modern DataOps and platform engineering principles.

Benefits

• medical, dental, vision, and prescription drug coverage
• access to Health Coach from GE Vernova, a 24/7 nurse-based resource
• access to the Employee Assistance Program, providing 24/7 confidential assessment, counseling and referral services
• GE Vernova Retirement Savings Plan, a tax-advantaged 401(k) savings opportunity with company matching contributions and company retirement contributions
• access to Fidelity resources and financial planning consultants
• tuition assistance
• adoption assistance
• paid parental leave
• disability benefits
• life insurance
• 12 paid holidays
• permissive time off
• Relocation Assistance Provided

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed