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Hermeus is a venture-backed defense aviation company reclaiming the lost art of rapid iterative prototyping to build the fastest aircraft in the world today. By prioritizing relentless hardware iteration, we deliver high-speed systems at the pace of the modern battlefield. We work with the Department of War to provide the high-speed capabilities our nation and its allies need to maintain a durable, asymmetric advantage. Hermeus is building a unified enterprise data platform as a core pillar of its digital transformation. This platform integrates ERP, Manufacturing, Engineering, HR, IT, and operational systems into a scalable foundation that supports decision-making, analytics, and long-term digital thread continuity. To ensure our data architecture scales with the complexity of our business, we are creating the foundational role of Enterprise Data Architect. This individual will establish and evolve the architectural foundation that ensures enterprise data is accurate, structured, governed, and ready for analytics and operational execution across the company. This role is expected to evolve in scope as the enterprise data platform matures, with increasing influence across business domains and enterprise technology strategy.
Job Responsibility
Define and evolve the enterprise data architecture across PLM, ERP, MES, HR, IT
Establish dimensional modeling standards (fact/dimension conventions) and canonical entity definitions
Design scalable integration patterns that support onboarding new systems without structural rework
Align operational and analytical models
Define and maintain canonical enterprise entities
Establish enterprise-wide naming conventions, data ownership standards, data definitions across systems and modeling governance
Partner with application owners to align upstream system design with downstream analytics needs
Strengthen ingestion reliability, monitoring, and change-detection patterns
Improve performance through indexing, partitioning, and workload optimization
Implement scalable incremental load strategies and reconciliation processes
Ensure platform stability as data volume and system count increase
Centralize business logic currently dispersed across BI models
Establish reusable semantic patterns that simplify reporting and metric consistency