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 Sr. Manager, Enterprise Data is responsible for leading delivery across Shield AI’s Enterprise Data team as the organization enables business domains onto its governed Databricks data platform.
This role manages the engineers and delivery work required to turn enterprise data strategy into usable, reliable data products. The Sr. Manager will establish effective delivery rhythms, guide domain prioritization, manage capacity and dependencies, and ensure that business needs are translated into clear, appropriately scoped work for the Data Engineering, Analytics Engineering, Domain Enablement, and Data Governance functions.
The role requires substantial data and analytics depth to assess delivery approaches, challenge unclear requirements, make appropriate tradeoffs, and ensure that the team produces durable, governed, production-ready outcomes rather than one-off reporting solutions.
The Sr. Manager partners closely with the Sr. Director of Enterprise Data & Architecture on strategy, operating model, investment priorities, executive engagement, and cross-functional alignment. The role partners with Platform Engineering and Enterprise Architecture, which remain direct functions of the Sr. Director, to ensure domain delivery is built on secure, reliable, scalable enterprise foundations.
What you'll do:
- Lead and develop the Enterprise Data delivery team, including Data Engineers, Analytics Engineers, Domain Enablement Engineers, and Data Governance specialists.
- Establish clear delivery operating rhythms for planning, prioritization, capacity management, roadmap tracking, dependency management, risk escalation, and stakeholder communication.
- Turn enterprise data strategy and business-domain priorities into sequenced, achievable delivery plans across platform onboarding, source integration, data-product delivery, semantic enablement, and governance.
- Partner with business leaders and domain stakeholders to shape intake, clarify intended outcomes, assess readiness, prioritize use cases, and establish realistic delivery expectations.
- Ensure domain work is appropriately scoped and sequenced, balancing near-term business value with the need for durable, governed, reusable data foundations.
- Coordinate delivery across Data Engineering, Analytics Engineering, Domain Enablement, and Data Governance; resolve dependencies and escalate decisions that require platform, architecture, security, infrastructure, or executive direction.
- Ensure team outputs meet expectations for production readiness, data quality, documentation, ownership, lineage, security, access controls, and maintainability.
- Review delivery plans, technical approaches, risks, and tradeoffs with technical leads; challenge work that creates unnecessary duplication, ungoverned data assets, or unsustainable operational burden.
- Partner with the Sr. Staff Data Engineer to align domain delivery to shared ingestion, transformation, and deployment patterns.
- Partner with the Staff Analytics Engineer to ensure domain assets align to enterprise semantic standards, metric definitions, and Gold-layer promotion expectations.
- Partner with the Platform / Data Reliability Engineer to ensure domain workloads meet platform standards for reliability, observability, cost management, environment promotion, and secure production operation.
- Partner with the Data Governance Specialist to ensure ownership, stewardship, classifications, metadata, lineage, quality expectations, and approvals are incorporated into delivery work.
- Hire, coach, develop, and retain a high-performing data team; establish role clarity, growth expectations, performance feedback, and appropriate technical leadership opportunities.
- Define and monitor practical measures of delivery health, including roadmap progress, throughput, time to enable new domains, adoption, quality trends, operational stability, and unresolved dependencies.
- Communicate delivery progress, material risks, investment needs, and tradeoffs clearly to business and technology leadership.
- Continuously improve the team’s delivery model as the enterprise data platform, domain portfolio, and organizational maturity evolve.
Required qualifications:
- 12+ years of experience across data engineering, analytics engineering, BI/data platforms, data architecture, or related technical data disciplines.
- 3+ years of experience leading and developing technical teams responsible for data, analytics, data products, or data-platform delivery.
- Demonstrated success leading delivery across multiple business domains and balancing competing stakeholder priorities.
- Strong understanding of modern data-platform concepts, including lakehouse architecture, data pipelines, Bronze/Silver/Gold patterns, dimensional modeling, semantic layers, data quality, metadata, lineage, and governed access.
- Hands-on experience with Databricks, including Delta Lake, Unity Catalog, Databricks SQL, Workflows, or related lakehouse capabilities.
- Ability to assess and challenge technical delivery approaches without needing to be the primary hands-on implementer for every solution.
- Experience translating business priorities into outcome-oriented roadmaps, scoped delivery plans, and realistic sequencing decisions.
- Experience partnering with business stakeholders, software engineering, cloud/infrastructure, security, governance, and architecture functions.
- Strong judgment in ambiguous, fast-moving environments with incomplete information and competing demands.
- Strong communication, organizational leadership, and stakeholder-management skills.
Preferred qualifications:
- Experience building or scaling an enterprise data function, data-product operating model, or multi-domain analytics platform.
- Experience leading data delivery for Supply Chain, Manufacturing, Finance, Program Finance, GTM, HR, Engineering, or other complex enterprise domains.
- Experience in aerospace, defense, autonomous systems, manufacturing, government, or another regulated and security-sensitive environment.
- Experience leading platform migrations, modernizations, ERP transformations, or enterprise data operating-model change.
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.