LIVE FEEDVIEWS 3UTC —
POSTING ACTIVE · REQ-A243A · FY26.Q3
Staff Data & Analytics Engineer, Domain Enablement (R6112)
[ COMPENSATION RANGE · ANNUAL · BASE ]
$160,000 – $240,000USD
MIDPOINT
$200,000
SPREAD
$80,000
LEVEL
LEAD
TECHNICAL STACK · 1 TAGS
§ 01
OVERVIEW
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.
§ 02
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.
§ 03
WHAT YOU'LL DO
- 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.
§ 04
REQUIRED QUALIFICATIONS
- 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 operating in controlled, export-sensitive, government, defense, or security-sensitive environments.
- 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.
§ 05
PREFERRED QUALIFICATIONS
- 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.
Full-time regular employee offer package:
Pay within range listed + Bonus + Benefits + Equity
Temporary employee offer package:
Pay within range listed above + temporary benefits package (applicable after 60 days of employment)
Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information.
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Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know.
QUESTIONS AND ANSWERS
- How much does the Staff Data & Analytics Engineer, Domain Enablement (R6112) at Shield AI pay?
- The posting lists a range of $160K–$240K per year. Ranges reflect what Shield AI publicly declared on the source posting.
- Where is this Staff Data & Analytics Engineer, Domain Enablement (R6112) role based?
- The role is based in Dallas, Texas.
- What experience does Shield AI expect for this role?
- The posting is tagged as a lead-level role, typically 7+ years of experience. Check the requirements section for specifics.
- Where is Shield AI headquartered?
- Shield AI is headquartered in San Diego, USA.
- How was this posting sourced?
- This role was pulled directly from Shield AI's Lever careers site. Apply links open in the employer's own ATS — no reposts or aggregator middleware.
[ APPLICATION ROUTE ]LEVER · External ATS
Apply links open in the employer's official ATS. Always verify recruitment messages on the company's careers page before sharing personal information.
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