Director, Technical Program Management

Grant Thornton•Dallas, TX

About The Position

Grant Thornton is seeking a Director of Technical Program Management to join the team. Approved office locations can be found below. We build products, not tickets. The Director of Technical Program Management makes sure that what we decide to build actually reaches users, in the shape we intended and on a timeline people can plan around. The portfolio spans products our own teams depend on to do their work and products our customers buy and use, and both deserve the same standard of engineering execution. The role sits at the center of the product engineering system. Innovation validates what is worth building, Platform builds durable shared capability once, Delivery assembles it into solutions for each market and audience, and Operations keeps it healthy in production. This Director turns those moving parts into one coherent plan: sequencing the work, surfacing dependencies before they become escalations, and holding a single honest view of what is shipping, when, and what is at risk. Increasingly that work is shaped by AI, both in the products we ship and in how the teams build them, and this role is expected to plan for AI work on its own terms rather than treating it as ordinary software with a different label. What This Pillar Owns The Technical Program Management pillar owns cross-team program execution and the engineering operating cadence that carries an idea from validated concept to a product increment in front of users. It covers program planning and sequencing, dependency and risk management across the pillars, the ship cadence and flow health of the squads, and launch readiness for each audience we serve. It does not decide the roadmap, own platform architecture, configure market instances or run production; it makes the teams that do those things move together.

Requirements

Twelve or more years in product engineering, with at least five leading technical program management or engineering delivery teams in a product organization.
A record of shipping software products that real users adopted, ideally including both internal-facing tools and externally used products.
Genuine technical depth: comfortable in architecture and design discussions, able to challenge an estimate or a dependency on the substance rather than the process.
Experience running large cross-functional programs spanning several engineering teams, product management and design.
Fluency in modern agile practice and flow metrics, used to improve delivery rather than to report activity.
Experience managing and developing program managers or scrum masters across distributed locations and time zones.
Working fluency with AI tooling in the delivery workflow, and judgment about where it helps and where it produces confident noise.
Experience program-managing AI or machine learning work, where quality is measured statistically, iteration is expected and a fixed-scope plan is the wrong instrument.

Nice To Haves

Experience with a platform-plus-solutions model, where shared capability is built once and deployed to many audiences, tenants or markets.
Working knowledge of continuous delivery, feature flags, experimentation and progressive rollout as everyday product engineering tools.
Exposure to product analytics and adoption measurement, and comfort using them to judge whether a launch actually worked.
Familiarity with how AI products are evaluated in practice, including evaluation sets, quality baselines, and the cost and latency trade-offs that shape what can be shipped.

Responsibilities

Turn roadmap commitments into sequenced, resourced program plans with named owners, real dates and an explicit definition of the user outcome being delivered.
Run the programs that span multiple squads and pillars, from validated concept through build to a product increment in front of internal or external users.
Map dependencies across Innovation, Platform, Delivery and Operations early enough to be designed around rather than escalated late.
Drive scope and sequencing trade-offs with product management and engineering leaders when reality moves, and make the consequences of each option clear.
Plan AI-enabled product work with the discovery and evaluation loops it genuinely requires, and hold the line on shipping only when quality has been measured against real user scenarios.
Lead the scrum masters and set a consistent standard for planning, backlog health, estimation and demo cadence across squads, without turning agility into ceremony.
Improve how work flows: shorten cycle time, limit work in progress, and reduce the handoffs and wait states that quietly cost more than any individual delay.
Instrument delivery with a small set of trusted metrics, and use them to spot systemic problems rather than to grade teams.
Clear the impediments that a squad genuinely cannot clear on its own, and fix the recurring ones at their source.
Put AI to work inside the delivery function itself, using it for planning support, status synthesis, risk detection and reporting, so the team spends its time on judgment rather than on assembling slides.
Own ship readiness for each product increment: what is in it, who it affects, how it is enabled, and what happens if it needs to be pulled back.
Coordinate the non-engineering half of a launch, working with product management, design, support and market teams on enablement, documentation and communications so users are not surprised by their own product.
Tune the bar to the audience: an internal tool can iterate in the open, while an externally used product carries commitments to customers that shape sequencing and timing.
Partner with Delivery and Operations on the first weeks after launch, then close out heightened support once the product is genuinely stable.
Engage with the architecture and the trade-offs directly: this role is expected to understand why a design choice creates a schedule risk, not merely to record that engineering said so.
Keep a live view of program risk with quantified impact on outcome, scope or date, and bring options to leadership rather than status.
Protect engineering focus by keeping reporting light, decisions fast and meetings few, and by absorbing coordination overhead so squads do not have to.
Program-manage AI work honestly: build and evaluation cycles that are inherently iterative, quality that is measured statistically rather than passed or failed, dependencies on models, data and evaluation sets, and cost and latency characteristics that shift after launch.
Build and lead a distributed team of technical program managers and scrum masters: hire, coach, set expectations and develop the next generation of program leaders.
Establish the minimum viable standards and templates the function needs to scale as the portfolio grows, and be the person leadership asks for a straight answer on how a program is really going.