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.
Lead discovery and end-to-end enablement for complex Supply Chain and Manufacturing domains initially, with flexibility to support other priority enterprise domains as business needs evolve.
Partner directly with Supply Chain, Manufacturing, and Operations leaders to understand business processes, decisions, source systems, reporting needs, metrics, and pain points; collaborate with Finance, Program Finance, Engineering, IT, Security, and other partners where processes, systems, or data intersect.
Translate ambiguous business needs into clear problem statements, prioritized use cases, phased roadmaps, technical designs, and achievable delivery plans.
Design and build governed data products across the data stack, including source-system assessment and integration requirements; Bronze, Silver, and Gold data assets; transformation logic; domain marts; curated datasets; semantic models; testing; documentation; and production-readiness controls.
Define canonical domain concepts, grain, facts, dimensions, conformed entities, historical treatment, business rules, and reconciliation approaches for high-value operational and analytical data.
Design and deliver governed Supply Chain and Manufacturing data models and analytical assets for concepts such as parts, materials, suppliers, purchase orders, demand, supply, inventory, work orders, production, quality, cost, and fulfillment, aligned to established enterprise patterns and standards.
Work across ERP, PLM, MES, MRP, procurement, manufacturing, quality, inventory, supplier, finance, and operational systems to create integrated and understandable data products.
Develop and optimize transformation pipelines using Databricks, SQL, Python, PySpark, Delta Lake, and related technologies as appropriate.
Apply enterprise ingestion, modeling, naming, semantic, quality, documentation, lineage, and promotion standards across Bronze, Silver, and Gold layers; identify where those standards need to evolve to support complex operational domains.
Partner with Data Engineering, Platform Engineering, and Data Governance to apply shared standards and establish the ingestion, reliability, security, access, lineage, metadata, stewardship, and quality controls needed for domain data products.
8+ years of experience in data engineering, analytics engineering, BI engineering, data architecture, or a blended data role.
Demonstrated experience independently delivering end-to-end data and analytics solutions—from source-system discovery and integration through governed, business-consumable data products.
Strong experience in at least one complex operational domain, such as Supply Chain, Manufacturing, Procurement, Planning, Logistics, Operations, Industrial, or Program Management.
Strong dimensional modeling and semantic design skills, including facts, dimensions, grain, conformed dimensions, historical treatment, and auditable business logic.
Hands-on production experience with Databricks, including Bronze, Silver, and Gold lakehouse patterns, Delta Lake, SQL, and Python and/or PySpark.
Experience integrating data from complex enterprise systems, such as ERP, PLM, MES, MRP, procurement, inventory, supplier, quality, production, or financial systems.
Ability to translate ambiguous business needs into practical delivery scopes, technical designs, and prioritized roadmaps.
Strong communication skills and comfort partnering directly with business and technical stakeholders.
Experience in aerospace, defense, aviation, autonomous systems, robotics, advanced manufacturing, industrial operations, or similarly complex and regulated environments.
Experience supporting Supply Chain or Manufacturing capabilities such as demand planning, supply planning, procurement, supplier performance, inventory and materials management, production, quality, maintenance, repair, or fulfillment.
Experience with systems such as SAP, Oracle, IFS, Deltek, Costpoint, PLM, MES, MRP, SCM, or comparable enterprise platforms.
Experience integrating operational measures with Program Finance, cost, inventory valuation, forecasting, planning, or program-performance reporting.
Experience operating in controlled, export-sensitive, government, defense, or security-sensitive environments.
