Sr Director, IT Strategic Transformation

The Gap•Dallas, TX

About The Position

Gap Inc. is seeking a Strategic Transformation Lead to drive two of its most consequential technology bets: the execution of its enterprise GCP Data and AI transformation, and the design and stand-up of an AI-native technology delivery entity built the emerging agentic development toolchain. This is a senior role for an executive-caliber operator who has done this before — someone who can anchor a complex, multi-year platform transformation while simultaneously helping Gap design what technology delivery looks like in an AI-native world. The two mandates are sequential in emphasis but parallel in mindset: get the data and AI foundation right and use that foundation to define and build the delivery model that runs on top of it.

Requirements

Proven success leading enterprise transformation programs in complex, matrixed organizations.
Strong ability to connect strategic ambition to executable plans, governance structures, and measurable outcomes.
Demonstrated ability to serve as a trusted advisor to senior executives during periods of change, modernization, or operating model evolution.
Passionate about leading teams, developing people and creating a culture of ownership and continuous improvement.
Strong technical fluency across modern cloud, data, platform, and enterprise technology environments.
Working understanding of data platforms, AI and analytics enablement, cloud ecosystems (GCP preferred), security and compliance considerations, and modern software delivery practices.
Familiarity with AI-native development toolchains and agentic SDLC frameworks.
Ability to engage credibly with engineering, architecture, infrastructure, and product leaders on technical tradeoffs and execution implications.
Deep experience running large, cross-functional programs with multiple workstreams, interdependencies, and executive stakeholders.
Strong command of portfolio planning, milestone management, risk governance, capacity alignment, and delivery reporting.
Track record of improving execution predictability, organizational transparency, and operational discipline.
Exceptional communication skills with the ability to distill complexity into clear, concise, decision-oriented narratives.
Strong executive presence and credibility with senior business and technology stakeholders.
Ability to unify divergent points of view, surface options and tradeoffs, and drive alignment without relying solely on direct authority.
Experience managing portfolio budgets, capital and operating planning, investment prioritization, and value realization.
Strong analytical orientation with the ability to use data to guide decisions, manage tradeoffs, and improve outcomes.
Comfortable operating at both strategic and operational levels simultaneously.
Strong capability in operating model design, organizational effectiveness, and stakeholder orchestration.
Experience standing up new organizational entities, AI centers of excellence, or capability organizations in enterprise environments.
Demonstrated ability to sustain momentum through ambiguity, competing priorities, and enterprise complexity.
15+ years of leadership experience spanning enterprise transformation, program leadership, technology strategy, or engineering and data operations.
Bachelor’s degree in a relevant discipline required.

Nice To Haves

Experience leading large-scale GCP, data, analytics, platform, or AI-related transformations — retail, consumer, or digital environments strongly preferred.
Experience designing or standing up AI-native delivery organizations, innovation entities, or new operating model structures.
Familiarity with modern enterprise delivery and planning tools such as Jira, Confluence, Smartsheet, Clarity, Power BI, or equivalent platforms.
Advanced degree, GCP certification, or program management certification advantageous.

Responsibilities

Translate Gap’s Data and AI vision into an executable multi-quarter transformation roadmap with clear sequencing, milestones, dependencies, and value measures tied to business outcomes — not technical deployments.
Convert high-level strategic objectives into actionable multi-year transformation plans with defined outcomes, operating principles, and success criteria.
Partner with executive leaders to align roadmap priorities with enterprise goals, business demand, and platform scalability needs.
Balance near-term delivery objectives with long-term architectural integrity and operating model maturity.
Drive integrated execution across platform modernization, analytics enablement, data operations, AI capabilities, and engineering delivery — across multiple teams, vendors, and strategic partners including our execution partners and Google.
Coordinate dependencies across product, engineering, architecture, infrastructure, security, and operations.
Bring structure and discipline to program delivery in a fast-moving, evolving transformation environment.
Ensure the transformation delivers durable business outcomes.
Serve as a trusted advisor to SVP and executive leadership on transformation progress, risk posture, tradeoffs, investment decisions, and sequencing — with the judgment to surface the hard things early.
Design and run executive governance forums — portfolio reviews, QBRs, steering committees, and escalation mechanisms — that produce decisions, not status updates.
Produce high-quality executive communications, decision memos, dashboards, and board-ready updates.
Enable leadership teams to make faster, better-informed decisions through structured reporting, forward-looking insights, and clear recommendations.
Help define and evolve the operating model required to scale Data and AI delivery across the enterprise.
Introduce or strengthen governance mechanisms for planning, intake, prioritization, capacity alignment, execution reviews, and issue management.
Improve transparency, accountability, and predictability across teams by standardizing reporting, delivery rhythms, and performance measures.
Foster cross-functional ways of working that enable business, product, data, and engineering teams to operate as one aligned system.
Build, inspire, and develop a high-performing team that embodies accountability, collaboration, and innovation.
Foster a culture of one team while modeling full ownership to delivery and outcomes expected.
Serve as a mentor and coach to emerging leaders—raising the organizational bar for performance and development.
Contribute to annual and quarterly planning processes for the transformation portfolio, including sequencing, prioritization, funding alignment, and value realization.
Maintain visibility into capital and operating budgets, partner and vendor investments, resource utilization, and portfolio health.
Partner with Finance and senior leaders to support business cases, investment prioritization, and transformation tradeoff decisions.
Ensure program execution remains tightly connected to budget discipline, delivery commitments, and measurable enterprise value.
Establish rigorous mechanisms for identifying, escalating, and resolving risks, dependencies, and execution bottlenecks.
Improve delivery confidence through strong forecasting, milestone management, capacity planning, and issue resolution.
Assess organizational readiness and help leaders proactively address change impacts, adoption risks, and execution constraints.
Define the structure, operating model, and capability architecture of an AI-native technology delivery entity — one built from the ground up the broader agentic development toolchain
Establish the overall AI operating model based on the existing transformation governance work: the AI-native SDLC, the toolchain governance model, delivery standards, quality and security posture, and the operating principles that make AI-driven delivery safe, repeatable, and measurable at scale.
Design the talent and resource model for an AI-native delivery shop — what roles exist, how they differ fundamentally from traditional engineering roles, what the human-to-AI ratio looks like across different delivery contexts, and how the org scales without scaling headcount linearly.
Define the relationship for the AI operating model and Gap’s existing engineering, product, and platform organizations — what moves into the new entity, what stays in the current model, and how the two operate in parallel during the transition.
Map the path from Gap’s current technology delivery operating model to the AI-native target state, with clear milestones, decision points, toolchain adoption gates, and value realization markers.
Partner with TMO, Engineering, Office of AI, and Enterprise & Solution Architecture to ensure the AI operating model is built on and extends Gap’s existing investments — Agentic SDLC, AI governance posture — rather than creating a parallel track that fragments the portfolio.
Establish the governance model that ensures AI-driven delivery meets Gap’s security, compliance, architecture, and quality standards — including how code generated by agents is reviewed, tested, deployed, and monitored in production.
Define year-one success measures — velocity, quality, cost per delivery unit, adoption rate across teams — and work backwards to the decisions that need to be made now to get there.
Develop the executive narrative and investment case required to move from concept to commitment — operating model options, cost and productivity framing, risk considerations, and a clear phased recommendation.
Design the business case with operating model options, cost and benefit framing, risk considerations, and a clear recommendation for the path forward.