Product Engineering Director

Grant Thornton is seeking a Product Engineering Director to join the team. Approved office locations can be found below. 

Role Overview

The Director of Product Engineering leads the Delivery vertical: the engineering teams that build and ship the products people actually use, whether that is a team inside the business or a customer who bought the product. This is the accountable engineering owner for the products in scope. The role answers for delivering them on time, within budget, and to the outcomes the product was designed to achieve, and it carries technical and engineering ownership of the product roadmap across the entire lifecycle, from the first design decision through every release to the point where a capability is retired.

Platform builds durable shared capability once; Delivery designs, writes and ships the solution on top of it and keeps shipping as the product evolves. The role owns the build, the quality of what gets built, and the pipeline that carries it to users, in an engineering organisation where AI is both a capability we ship inside our products and a tool that changes how we build them. It is also the discipline that keeps the product coherent: audience-specific needs are met through configuration, feature flags and workflow rules rather than forked code, and where a real need cannot be configured, this role makes the case for new platform capability rather than quietly building an exception.

What This Vertical Builds

Delivery is where the product gets built and shipped. The vertical designs and implements the solution on top of shared platform capability, writes and reviews the code that belongs at the solution layer, builds the integrations, data flows and workflow logic each audience needs, proves quality through automated testing and performance work, and ships to users through a modern delivery pipeline. Variation between audiences is expressed in configuration, feature flags and workflow rules rather than forked code, so the product stays one product no matter how many groups use it.

Key Responsibilities

Accountability for Delivery, Budget and Outcomes

• Answer for delivering the products in scope on time: make the commitment with the team, plan to it honestly, and raise a credible warning early when the date is genuinely at risk rather than late when it is unrecoverable.
• Own the engineering budget for the vertical, covering people, tooling, environments and the run cost of what is built, and make the cost consequences of a design or staffing choice visible before it is made rather than after.
• Deliver the product to the outcomes it was designed to achieve, holding the intent through implementation so that what ships still does the job for users, and flagging honestly when a technical constraint would compromise that intent.
• Measure what happened after launch against what was promised, with product management, and feed the difference back into the next cycle.

Technical Ownership of the Roadmap and Lifecycle

• Serve as the engineering owner of the product roadmap: assess feasibility, size the work realistically, sequence it around technical dependencies and make the trade-off between speed now and cost later explicit rather than implicit.
• Maintain the technical roadmap alongside the product roadmap, covering architecture runway, platform adoption, technical debt, security and performance work, and defend the share of capacity it needs.
• Own the products technically across the whole lifecycle, from initial design through build, launch and successive releases to deprecation and retirement, including the migration path when a capability is replaced.
• Partner with product management as the technical half of the roadmap conversation: shaping what is possible, what it will cost and when it can land, so commitments are made with engineering reality already in them.

Building and Shipping the Product

• Own the build end to end for the solutions in scope: design, implementation, integration, test automation and shipping to production.
• Hold engineering quality where it is decided, in code review, test coverage, performance work and observability built into the product rather than bolted on afterwards.
• Ship through continuous delivery: small, frequent, reversible releases behind feature flags, with progressive rollout to internal users before wider exposure wherever that is possible.
• Stay with a launch until it is genuinely stable, fixing what real usage exposes and folding the lessons back into the next build.

Engineering Standards and Platform Partnership

• Set the engineering standards for the vertical: coding practice, review expectations, automated test strategy, environment and branching approach, and the definition of done that applies to every squad.
• Keep solution-layer code thin and intentional, resisting the local workaround that would fragment the product and making the case for shared platform capability when the same need shows up more than once.
• Partner with Platform engineering on architecture, APIs and extension points, contributing real design input rather than consuming whatever ships, and route defects that belong in the core back to the team that owns it.
• Industrialize what Innovation proves out: take a validated prototype and rebuild it to the standard a product people depend on actually requires.

AI in the Product and in the Build

• Build AI capability into the products themselves where it serves a real user need, from assistive features and natural-language interfaces to intelligent automation of the workflows our users spend their day in.
• Hold AI features to the same engineering standard as everything else: evaluation sets and measurable quality baselines before launch, monitoring of accuracy and cost in production, graceful behaviour when the model is wrong, and a human in the loop wherever the decision warrants one.
• Make the engineering teams themselves AI-native, using coding assistants, automated test generation, code review support and documentation tooling to raise throughput and quality, while keeping human accountability for what ships.
• Work within the guardrails that responsible AI demands, covering data handling, privacy, model and vendor choice, bias and transparency, in partnership with Platform, security and legal rather than in isolation.

Engineering Leadership

• Lead the engineering managers and senior engineers of the vertical, and stay close enough to the work to review a design, read a pull request and know whether an estimate is honest.
• Invest in the engineering system itself: pipelines, test automation, environment provisioning and tooling, so the second and tenth build cost a fraction of the first.
• Manage capacity, sequencing and staffing across concurrent builds, and make the trade-off calls openly when demand exceeds what the teams can do well.
• Hire, coach and develop engineers and engineering leaders across distributed locations, and build depth in the skills the product will need next.

Requirements

• Twelve or more years building software products, with at least five leading engineering managers and senior engineers at director or equivalent level.
• Hands-on engineering background, still able to review an architecture, read code and judge a design trade-off on its merits.
• A record of shipping and stabilising products that real users depend on, internal or external, not only of managing teams that did.
• Experience building on a shared platform or multi-tenant product, where audience-specific needs are met through configuration and extension rather than bespoke code.
• Strong command of modern delivery engineering: continuous integration and delivery, automated testing, feature flags, environment strategy and observability.
• Experience leading distributed engineering teams, with a track record of hiring and developing engineers and engineering leaders.
• Experience as the technical owner of a product roadmap across its lifecycle, including feasibility assessment, architecture runway, technical debt strategy and end-of-life decisions.
• A record of owning delivery commitments end to end, including schedule and budget accountability for a product or a substantial programme of engineering work.
• Experience adopting AI development tooling across a team, including how to raise throughput without lowering the bar on code quality or review.
• Practical experience building or leading the build of AI-enabled product features, and a clear-eyed view of where they add value and where they do not.

Preferred

• Experience delivering to multiple audiences, regions or tenants from a single codebase, including where local compliance obligations apply.
• Depth in one or more of API design, data engineering, workflow and rules platforms, or front-end product engineering.
• Experience taking work from a discovery or MVP team and rebuilding it to production standard, including the hardening that makes it supportable.
• Familiarity with the practical side of AI engineering: prompt and context design, retrieval, evaluation harnesses, latency and cost management, and the failure modes that only appear in production.

The base salary range for this position is between $184,000 and $260,000. Placement within the pay range is at Grant Thornton’s discretion, and it is based on multiple factors, including but not limited to, job-related knowledge/skills, experience, business needs, progression within the role, geographic location, and internal equity. At Grant Thornton, compensation decisions are dependent upon the facts and circumstances of each position and candidate.