Senior Manager, Enterprise Data

Shield AI is a U.S. defense-technology company developing mission-autonomy software and autonomous aircraft for military and allied operations.

San Diego, United States
About Shield AI

Founded in 2015, Shield AI builds Hivemind autonomy software and V-BAT and X-BAT aircraft for operations in contested, GPS- and communications-denied environments. Its current site also presents Aechelon synthetic-reality simulation and Vision Systems detection and tracking products.

View jobs by Shield AI

Skills

About the Role

You will lead and develop enterprise data delivery teams across data engineering, analytics engineering, domain enablement, and data governance. You will turn business priorities into delivery plans, coordinate dependencies, ensure data quality and production readiness, manage stakeholders, and improve delivery operations.

Requirements

  • 12+ years of experience in data engineering, analytics engineering, BI or data platforms, data architecture, or related data disciplines
  • 3+ years leading technical data, analytics, data-product, or data-platform teams
  • Experience leading delivery across multiple business domains
  • Knowledge of lakehouse architecture, data pipelines, Bronze/Silver/Gold patterns, dimensional modeling, semantic layers, data quality, metadata, lineage, and governed access
  • Ability to assess technical delivery approaches
  • Experience translating business priorities into roadmaps and delivery plans
  • Experience partnering with business, software engineering, infrastructure, security, governance, and architecture stakeholders
  • Communication, organizational leadership, and stakeholder-management skills

Responsibilities

  • Lead and develop enterprise data delivery teams
  • Establish delivery planning, prioritization, capacity management, and risk-escalation practices
  • Translate business priorities into sequenced data delivery plans
  • Partner with stakeholders to prioritize use cases and set delivery expectations
  • Coordinate delivery across data engineering, analytics engineering, domain enablement, and governance
  • Ensure production readiness, data quality, documentation, lineage, security, and access controls
  • Review technical plans, risks, and tradeoffs
  • Hire, coach, develop, and retain data professionals
  • Monitor delivery health and communicate progress and risks
  • Improve the data delivery model

Benefits

  • Bonus
  • Benefits
  • Equity
  • Temporary benefits package after 60 days of employment