Shieldai
Staff Data & Analytics Engineer, Domain Enablement (R6112)
Dallas, Texas
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- Role family
- Data & ML
- Seniority
- Lead / management
- Country
- US
- Work mode
- Remote-friendly
- First seen by hirly
- 25 Sept 2026
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the posting
Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai . Follow Shield AI on LinkedIn , X , Instagram , and YouTube .
Job Description:
The Staff Data & Analytics Engineer, Domain Enablement is a senior, hands-on role responsible for enabling complex business domains on Shield AI’s enterprise data platform.
This person will lead discovery, scope high-value data and analytics opportunities, and deliver governed data products that make operational data more usable, trustworthy, and scalable for business decision-making. The role works across the data stack—from understanding source systems and defining integration needs through transformation, modeling, semantic design, quality controls, documentation, and business consumption.
The initial focus for this role is Supply Chain and Manufacturing, including the systems, processes, and data required to support planning, procurement, suppliers, materials, inventory, production, quality, fulfillment, cost, and program operations. Over time, the role may expand to support other complex domains as enterprise priorities evolve.
This is not a dashboard-development role. The Staff Data & Analytics Engineer will establish the domain data foundations and technical direction needed to deliver durable data products and accelerate future use cases. The ideal candidate combines strong technical depth with the ability to work directly with business partners, clarify ambiguity, and turn complex operational needs into practical, staged solutions.
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