Director, AI, Analytics & Engineering

Director, AI, Analytics & Engineering

Location:

United States (3 days in the Everett office)

The Director, AI, Analytics & Engineering leads the enterprise AI, data, analytics, and AI/data engineering function within the CIO organization, with end‑to‑end accountability for turning AI experimentation into secure, governed, production‑grade capabilities that deliver measurable business outcomes. This role owns enterprise data platforms and analytics, AI platforms and enablement, AI production delivery, and the operating model required to scale adoption across IT and business teams.

The leader serves as a primary partner to product and engineering organizations to promote AI reuse, shared platforms, and consistent delivery standards, while ensuring AI use is responsible, compliant, and aligned to enterprise governance and decision rights.

• Own strategy, delivery, and scalability of enterprise analytics, data platforms, AI solutions, and data/AI engineering capabilities, with accountability for measurable business outcomes.
• Define and drive the enterprise AI roadmap and execution plan, converting pilots and proofs‑of‑concept into high‑value production solutions embedded into business workflows.
• Establish and operate a clear AI intake and engagement model, including standardized intake channels, feasibility assessment, benefit quantification, and resourcing decisions tied to enterprise priorities.
• Ensure qualified AI requests enter portfolio visibility and resource allocation mechanisms (e.g., Lean Portfolio Management), and drive prioritization tradeoffs to maximize impact.
• Set decision rights and enforce accountability boundaries across AI platforms and solutions: technical standards and platform roadmap owned by the AI team; risk acceptance and compliance owned by Security/Privacy/Legal; outcomes and adoption owned by business owners.
• Enforce governed AI enablement across cloud environments by defining technical standards, reference architectures, and guardrails for data access, model use, logging, monitoring, and auditability.
• Partner with Security, Privacy, and Legal to operationalize responsible AI practices, including acceptable use expectations and governance processes for approving internal AI tools and features.
• Reduce AI and analytics tool sprawl by consolidating platforms, retiring shadow solutions, and standardizing reusable patterns and shared components.
• Own the enterprise data warehouse and analytics ecosystem, ensuring trusted data, scalable pipelines, consistent data quality, and decision‑ready analytics products and dashboards.
• Establish engineering excellence for AI and data products, including reliability practices, lifecycle management, availability/performance expectations, and cost efficiency.
• Define and run standard work for delivery planning, execution cadence, escalation, and executive reporting to ensure predictable outcomes and rapid issue resolution.
• Own value realization and success metrics for AI and analytics delivery, including business value delivered, platform adoption/usage, time‑to‑MVP and time‑to‑production, quality/risk metrics, and reuse of shared platforms and components.
• Own portfolio and financial accountability for AI and analytics investments, including budget planning, vendor/tooling decisions, and run vs. grow allocation aligned to outcomes.
• Evangelize AI practices across the enterprise through office hours, enablement, community forums, and practical guidance that accelerates adoption while keeping governance embedded in delivery.
• Serve as the CIO organization’s executive liaison to product and engineering teams to promote AI adoption, shared patterns (platform‑first), and scalable productionization approaches.
• Build and lead a high‑performing organization across AI engineering, data engineering, analytics, and platforms; establish hiring profiles, capability plans, and a vendor strategy that strengthens internal competence over time.
• Clearly define what is out of scope and enforce it (e.g., no AI delivery without a defined business owner and success metrics; no enablement outside approved governance guardrails; no long‑term…