Chief AI & Data Architect

Pacific Gas And Electric Company•Oakland, CA

1d•$192,800 - $277,150•Hybrid

About The Position

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.

Requirements

BA/BS degree in Computer Science, Engineering, Business or related field or equivalent experience.
12 years of enterprise architecture experience.

Nice To Haves

15+ years of leadership experience across data, analytics, AI, or enterprise technology.
Proven experience delivering enterprise‑scale AI and data programs in complex, regulated environments.
Strong understanding of data modeling, cloud platforms, AI/ML lifecycle management, and risk controls.
Executive leadership presence with the ability to influence across different lines of business including operations, and IT.
MA/MS in Computer Science, Information Systems, Information Security or other Technology Discipline.
Experience with specific technologies, systems and platforms related to a domain or associated sub-domain.
Experience with hardware, networks, software technologies, applications, and modeling techniques related to a domain or associated sub-domain.
Experience consulting with IT leadership on creating a strategic vision and direction with specific technologies, systems and platforms related to a domain.

Responsibilities

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, and AI-enabled analytics and decision support.
Reduce duplication and fragmentation across AI and analytics efforts.
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), and Real-time decisioning (streaming architectures).
Advocate for architecture patterns that enable structured and unstructured data integration, metadata-driven pipelines, and 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, and Metadata and semantic layers to support AI explainability.
Ensure consistent handling of structured vs. unstructured data (documents, images, logs, transcripts) and embeddings and vector data (for GenAI).
Promote “build once, reuse many” data principles.
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.
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.
Serve as the enterprise technical authority on AI and Data for executive leadership, regulators, and the Board.
Prepare executive recommendations, investment cases, and decision materials.
Act as a strategic advisor to executives on AI opportunities and implications.
Translate complex technical topics into clear, decision‑oriented executive insights.
Monitor external technology, regulatory, and industry trends to inform strategy.
Facilitate alignment across business units and corporate functions.
Resolve conflicts around priorities, ownership, funding, and standards.
Lead or support steering committees and leadership forums related to AI.