Data Engineering Leader, Supply Chain Digital Automation

Job Description SummaryBuild 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.

Job Description

Industrial Data Platform Architecture

• 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.

Industrial Semantic Modeling and Contextualization

• 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.

Edge Data Engineering and OT Connectivity

• 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.
• High-Performance Industrial Data Processing: Build pipelines capable of processing high-frequency industrial telemetry at scale, ensuring low latency for operational decision-making use cases.

AI-Ready Data Infrastructure

• 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.

Data Observability and Reliability Engineering

• 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.

Multi-Plant Data Platform Scaling

• 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.
• Standards Enforcement (Unified Namespace): 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.

OT Data Security Architecture

• 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

Data Products and Operating Model

• 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.

Build and Lead the Industrial Data Engineering Organization

• 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.

• 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.

• 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.

For candidates applying to a U.S. based position, the pay range for this position is between $116,600.00 and $194,200.00. The Company pays a geographic differential of 110%, 120% or 130% of salary in certain areas. The specific pay offered may be influenced by a variety of factors, including the candidate’s experience, education, and skill set.

Bonus eligibility: discretionary annual bonus.

This posting is expected to remain open for at least seven days after it was posted on May 14, 2026.

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

GE Vernova Inc. or its affiliates (collectively or individually, “GE Vernova”) sponsor certain employee benefit plans or programs GE Vernova reserves the right to terminate, amend, suspend, replace, or modify its benefit plans and programs at any time and for any reason, in its sole discretion. No individual has a vested right to any benefit under a GE Vernova welfare benefit plan or program. This document does not create a contract of employment with any individual.