Data & AI Technology Delivery Lead - Vice President

In the Technology division, we leverage innovation to build the connections and capabilities that power our Firm, enabling our clients and colleagues to redefine markets and shape the future of our communities. This is a Prin Technology Admin Office position at the Vice President Level, which is part of the job family responsible for managing administrative tasks related to technology infrastructure and services, ensuring smooth operations and support for the organization's technology needs. The Data & AI Technology Delivery Lead is a hybrid technology leadership role combining Fleet Enablement, Service Delivery Management, program execution, and Data & AI domain leadership. The role is responsible for translating the Data & Analytics strategy into coordinated execution across engineering teams, product owners, infrastructure partners, control functions, business stakeholders, and strategic vendors. The individual will provide centralized ownership of planning, prioritization, delivery governance, stakeholder communication, and operational readiness across the Data & AI platform portfolio. Unlike a traditional project-management role, this position requires sufficient technical depth to understand enterprise data platforms, AI capabilities, architecture dependencies, production-readiness requirements, and governance controls. The individual will act as the connective layer between technology strategy and execution, ensuring that priority capabilities move from evaluation through approval, onboarding, and production adoption. This role builds on the established Fleet Enablement model, which includes roadmap alignment, dependency management, blocker removal, fleet ceremonies, metrics, external-partner engagement, and continuous improvement What you’ll do in the role: 1. Fleet Enablement and Strategic Execution

• Partner with Fleet and technology leadership to translate strategic priorities into structured roadmaps, bodies of work, milestones, and measurable outcomes.
• Coordinate planning and execution across Data & Analytics squads, product teams, operations, architecture, cybersecurity, legal, procurement, and control functions.
• Maintain an integrated view of priorities, dependencies, capacity constraints, delivery risks, and critical decisions.
• Facilitate fleet-level governance and operating forums, including roadmap reviews, leadership updates, planning sessions, and cross-product working groups.
• Identify and remove organizational, technical, or process blockers that affect delivery.
• Promote consistent execution practices and continuous improvement across the fleet.
• Ensure squads remain aligned to approved priorities, roadmaps, and key performance indicators. These responsibilities align with the Fleet Enablement framework’s expectations for managing dependencies, removing blockers, facilitating ceremonies, tracking metrics, supporting roadmap delivery, and collaborating with external partners 2. Service Delivery Management
• Serve as a central point of accountability for the delivery and ongoing operation of Data & AI platform capabilities.
• Establish and maintain transparent intake, prioritization, planning, and delivery-tracking processes.
• Manage and prioritize work across platform hygiene, core functionality, service improvements, control remediation, client onboarding, and strategic AI enablement.
• Track commitments, milestones, risks, incidents, dependencies, remediation actions, and production-readiness requirements.
• Coordinate delivery across engineering teams, product owners, infrastructure partners, vendors, and internal clients.
• Provide clear escalation of delivery risks, resource constraints, service issues, and decisions requiring leadership attention.
• Support service governance, lifecycle management, capacity planning, operational readiness, resilience, and control obligations.
• Balance strategic change with operational stability, regulatory requirements, and platform sustainability. 3. Data & AI Technology Leadership
• Provide technology leadership across enterprise data, analytics, and AI platforms, including cloud data platforms, BI and analytics, data integration, and AI-enabled engineering solutions.
• Develop a working understanding of platform architectures, capabilities, limitations, governance requirements, and production-readiness dependencies.
• Translate business and technology requirements into actionable delivery plans for engineering and operational teams.
• Coordinate the assessment and enablement of emerging platform capabilities from initial evaluation through architecture, control review, production readiness, adoption, and ongoing service management.
• Support initiatives involving platforms such as Snowflake, Databricks, Dataiku, Power BI, MongoDB, and related enterprise Data & AI services.
• Partner with engineering and architecture leads to identify cross-platform dependencies, technical risks, and required design decisions.
• Help teams choose the appropriate platform or capability based on business requirements, control obligations, cost, scalability, and operational supportability.
• Maintain consolidated views of platform status, approved capabilities, emerging features, use cases, dependencies, and adoption paths. The current Data & AI portfolio spans governed enterprise data platforms, semantic foundations, foundation models, agent orchestration, AI operations, observability, and AI economics. 4. Governance, Risk, and Production Readiness
• Coordinate security architecture, legal, compliance, data-governance, and operational-readiness activities for new platform capabilities and integrations.
• Ensure required approvals, control evidence, architecture artifacts, action plans, and remediation items are clearly owned and tracked.
• Manage dependencies across policy, access controls, data classification, audit logging, vendor integrations, cost controls, and service monitoring.
• Partner with control functions and engineering teams to move capabilities through evaluation, build approval, production approval, and controlled adoption.
• Identify gaps early and establish clear remediation plans, owners, milestones, and escalation paths.
• Ensure that AI enablement is delivered within approved enterprise governance and risk frameworks. Existing Data & AI work includes centralized intake and governance, cross-product control accountability, security-architecture alignment, AI workload orchestration, and coordination with policy and control stakeholders. 5. Stakeholder and Executive Engagement
• Act as a trusted interface among senior leadership, engineering teams, product owners, control partners, business stakeholders, and vendors.
• Prepare concise, decision-oriented reporting covering progress, risks, blockers, dependencies, required decisions, and next steps.
• Translate complex technical topics into clear business impact, adoption considerations, and leadership decisions.
• Coordinate executive materials, platform updates, governance submissions, meeting briefs, technology landscapes, and strategic presentations.
• Ensure stakeholder expectations are realistic and aligned with technical capacity, controls, and delivery dependencies.
• Maintain effective communication across technical and nontechnical audiences. The role’s current operating context includes preparing consolidated platform updates, capability summaries, technology landscapes, leadership materials, newsletters, meeting notes, and vendor briefings. 6. Vendor and Partner Management
• Coordinate engagement with strategic technology vendors and internal partner organizations.
• Organize technical deep dives, roadmap reviews, demonstrations, issue-resolution sessions, training, and executive engagements.
• Track vendor commitments, product gaps, feature requests, delivery dependencies, and follow-up actions.
• Consolidate feedback from engineering, operations, architecture, control partners, and users into