Director, AI, Analytics & Engineering

Title: DirectorAI Analytics & Engineering

Location: United States (3 days in the Everett office)

Role Summary
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.

Key Responsibilities
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 organizations 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 ownership of bespoke non-strategic solutions).
Communicate crisply with senior leadership on outcomes delivered investment tradeoffs risks compliance posture and progress against delivery KPIs (on-time/on-scope time to production quality and cost per unit of delivery).

Role Characteristics
Enterprise-wide leadership role with direct accountability for platforms governance delivery operating model and measurable outcomes across AI analytics and data engineering.
Balances innovation and experimentation with production rigor compliance and cost discipline.

Qualifications and Experience
15 years of leadership experience across AI data engineering analytics and modern platform delivery.
Proven track record scaling AI from pilots to enterprise-grade secure production solutions with repeatable delivery patterns.
Deep experience with enterprise data platforms data warehousing pipelines and analytics product delivery (trusted metrics dashboards decision-ready insights).
Demonstrated ability to establish AI governance decision rights responsible AI practices and embedded compliance mechanisms.
Experience building and running intake prioritization and engagement models that quantify benefits and drive portfolio decisions.
Strong engineering leadership capability to set standards for reliability lifecycle management and operational readiness of AI/data products.
Proven executive presence and ability to partner with product and engineering organizations to embed AI capabilities into products and workflows.
Strong financial and portfolio management skills including investment tradeoffs vendor/tool strategy and outcome-based value tracking.

Success Measures

• AI and analytics investments consistently deliver measurable business outcomes with clear ownership quantified value and disciplined prioritization.
• AI governance enables speed with trust through embedded guardrails clear decision rights and responsible AI adoption at scale.
• Data platforms and analytics produce trusted decision-ready insights with strong reliability reuse and reduced tool sprawl.
• Time-to-MVP and time-to-production improve while quality compliance posture and cost efficiency remain strong and visible to leadership.

Scope Guardrails
This role is accountable for enterprise AI analytics and data platforms governance operating model and value realization within the CIO organization. It does not own product roadmaps product P&L or embedded product engineering teams which remain the responsibility of Product and Engineering organizations. The role enables governs and scales AI capabilities through shared platforms standards tooling and delivery models while partnering with Product and Engineering teams to embed AI into products and workflows. The role does not operate as a centralized service for bespoke or oneoff solutions and will not approve or deliver AI initiatives without a defined business owner quantified outcomes and adherence to enterprise governance. The role has authority to approve prioritize standardize or retire AI and analytics platforms and tools but adoption success and business impact are jointly owned with business and product leaders.