Data Engineering Lead

The role involves leading the design and building of the data foundation for emerging technology programs, including architecture, ingestion, integration, and engineering of data across enterprise systems, connected devices, operational platforms, partner ecosystems, and advanced technology environments.

• Proven experience in data architecture and data engineering in complex, multi-source environments
• Strong hands-on experience building modern data pipelines and integration frameworks
• Experience with cloud-based data platforms and distributed data processing
• Strong knowledge of batch, streaming, and event-driven architectures
• Experience with data modeling for operational and analytical use cases
• Experience integrating machine, device, telemetry, or operational data into broader data platforms
• Strong understanding of APIs, interoperability, and system integration patterns
• Ability to operate as both architect and hands-on delivery lead
• Strong communication skills across business and technical stakeholders

Nice to have:

• Experience in frontier technology, industrial, infrastructure, advanced engineering, or innovation-led environments
• Familiarity with edge-to-cloud patterns, digital twins, simulation data, or AI-ready platform design
• Experience with metadata, lineage, and governance integration
• Experience in highly regulated, operationally sensitive, or multi-party ecosystems

• Define and own the end-to-end data architecture across source systems, streaming pipelines, storage, transformation, APIs, and consumption layers
• Design and implement scalable data pipelines for structured, semi-structured, unstructured, and real-time data
• Integrate data from enterprise applications, devices, sensors, operational systems, third-party providers, and partner ecosystems
• Establish canonical data models and integration patterns across core entities such as assets, events, locations, products, devices, users, or operational processes
• Partner with security and governance leads to embed control, access, lineage, and quality requirements into the architecture
• Support use cases requiring real-time or near-real-time ingestion and processing
• Drive interoperability across fragmented technical and business environments
• Establish engineering standards, DataOps practices, monitoring, and reliability controls
• Enable downstream analytics, AI, simulation, and data product consumption
• Provide technical leadership across platform choices, implementation sequencing, and delivery tradeoffs