Chief AI & Data Architect

Requisition  #

Job Category:
Information Technology

Job Level: Director/Chief

Business Unit:
Information Technology

Work Type:
Hybrid

Job Location:

Oakland

Position Summary

The Chief AI & Data Architect is accountable for the enterprise‑wide strategy, governance, and value realization of Artificial Intelligence, Advanced Analytics, and Data. This role ensures that data is trusted, governed, reusable, and AI‑ready, and that AI capabilities are deployed safely, compliantly, and at scale across a regulated enterprise. As this is a director level role, this person typically does not own all enterprise AI execution directly, but they orchestrate the strategy, prioritization, standards, and cross-functional alignment needed to make AI investments produce measurable business outcomes.

The Chief serves as the bridge between data foundations and AI‑driven outcomes, ensuring alignment across business strategy, technology platforms, risk management, and regulatory obligations.

This position is hybrid, working from your remote office and the Oakland General Office Headquarters.

Reporting

Reports into the Senior Director, Enterprise Strategy & Architecture.

Job Responsibilities

Enterprise AI & Data Strategy

• Define and own the integrated AI and Data strategy, roadmap, and operating model aligned with enterprise goals and regulatory commitments.
• Partner with leaders to prioritize AI and data use cases that deliver measurable value (safety, reliability, efficiency, customer outcomes).
• Ensure AI investments are grounded in strong data foundations and avoid unmanaged experimentation.
• Develop the enterprise AI vision, principles, and multi-year roadmap
• Align AI priorities to business strategy, growth goals, cost optimization, risk reduction, customer experience, and operational efficiency
• Identify where AI should be used—and where it should not be used
• Establish standards across:
• Generative AI
• Predictive AI / machine learning
• Automation / intelligent workflows
• AI-enabled analytics and decision support
• Reduce duplication and fragmentation across AI and analytics efforts.

Data Architecture

• Serve as owner for enterprise data architecture including developing strategy, standards
• Ensure data policies, standards, and controls support AI/ML, GenAI, and analytics use cases.
• Establish standards for Data Products
• Ensure the enterprise data architecture is fit-for-purpose for AI at scale, not just reporting.
• Define the target-state data architecture principles to support AI (e.g., data products, data mesh/fabric, feature-ready data layers)
• Align data architecture to AI use cases such as:
• GenAI (context + retrieval layers)
• ML models (training + feature pipelines)
• Real-time decisioning (streaming architectures)
• Advocate for architecture patterns that enable:
• Structured and unstructured data integration
• Metadata-driven pipelines
• High-quality, reusable datasets for AI
• Ensure AI strategy is grounded in realistic data capabilities and constraints
• Define and enforce enterprise data standards that make AI scalable and reusable.
• Define standards for:
• Data modeling approaches (e.g., canonical models, domain-oriented models)
• Data product design (ownership, SLAs, discoverability)
• Feature engineering reuse and standardization
• Metadata and semantic layers to support AI explainability
• Ensure consistent handling of:
• structured vs. unstructured data (documents, images, logs, transcripts)
• embeddings and vector data (for GenAI)
• Promote “build once, reuse many” data principles

AI Platform, Architecture & Delivery

• Own strategy for AI and data platforms, including model lifecycle management, data pipelines, and AI enablement.
• Ensure AI and data solutions are secure, scalable, auditable, and cost‑effective.
• Partner with all areas of IT to define reference architectures and approved patterns.

Governance, Risk & Responsible AI

• Establish and enforce AI frameworks, including intake, classification, approval gates, and production readiness.
• Operationalize Responsible AI principles (privacy, transparency, explainability, human oversight).
• Collaborate closely with Legal, Cybersecurity, Privacy, Compliance, and Risk functions to ensure regulatory alignment.

Executive & Board Engagement

• Serve as the…