Director of Engineering

The Position:

Y Gov Pilot is a Gov Tech company that is growing rapidly and leverages AI heavily in order to meet aggressive roadmap targets with high quality. We are looking for a dynamic leader that understands how to match talent and capacity with business problems, and deliver high quality software at the pace that AI now enables.

You are the operational counterweight to a VP of Engineering who stays deliberately hands-on in architecture and MVP development. That split only works if the operational half is held by someone senior enough to run it without escalation.

You will need real technical depth. You cannot arbitrate a dependency conflict, judge a delivery risk, or defend a staffing trade-off without understanding the systems underneath it. What you will not need is to be the most senior technical voice in the room.

We are an AI-native engineering organization. Governing and scaling that practice is a core responsibility of this role, not a side project.

• Delivery across three product lines. End-to-end visibility and accountability: roadmap tracking, release coordination, cross-team dependencies, and risk escalation. One source of truth leadership can read without a translator. A disciplined incident and on-call program across time zones, with tracked postmortem follow-through.
• Engineering metrics. Define and instrument the KPIs that drive decisions: cycle time, lead time for changes, deployment frequency, change failure rate, MTTR, escaped defects, uptime against SLOs, and team health. Automate the reporting so it does not live in your head. Turn signal into a narrative leadership can act on, including what decision is needed and by when.
• Partnership with Product. Build a genuine peer relationship with the Director of Product. Own the capacity model that makes roadmap commitments credible, and surface trade-offs at the point of commitment rather than at the deadline.
• Staffing and capacity planning. Allocation and coverage across all three lines, with a repeatable framework rather than case-by-case negotiation. Partner with Product and Finance on headcount forecasting. Confirm projects are fully staffed before work begins, and stop the ones that are not.
• People leadership. Directly manage and develop engineering managers and leads. Own hiring, onboarding, and performance management. Build a management layer that operates consistently across geographies.
• AI-native engineering. Drive adoption of agentic coding tools. Set the standard for where AI belongs in planning, implementation, and review, and where human review stays mandatory. Own the governance: code provenance, security review, and IP and data handling. Report measured delivery impact, not activity.
• AI ROI
  . Partner with the executive AI sponsor to quantify the returns of AI-assisted development. Own the engineering evidence by balancing fully loaded costs against delivery outcomes like cycle time and quality, and establish governance for code provenance, human review boundaries, and data handling.

What You Bring:

• 10 or more years in software engineering, including 3 or more at Director level or senior engineering management.
• Delivery ownership across multiple concurrent products, not one team against one roadmap.
• Proven leadership of distributed global teams, including managing managers.
• Real technical depth. You have shipped software and can evaluate system design and technical risk on your own.
• Rigorous program and operations management. You leave systems behind that outlast you.
• Hands-on Git Hub experience, including Projects, Actions, and workflow design. Migration leadership strongly preferred.
• Working fluency with AI coding tools and a clear point of view on how they change review practices and quality control.
• Fluency in engineering metrics: what to instrument, what it indicates, and how to keep the program from being gamed.
• Executive communication. You translate engineering execution into business outcomes.
• Track record of a real peer partnership with a Director or Head of Product.

Preferred: Scale-up growth experience. Blended FTE and contractor staffing models. Govtech space familiarity. Background in multiple domains.

Delivery predictability against forecast. Movement in cycle time, deployment frequency, and change failure rate. Uptime against SLOs and incident response times. Projects fully staffed ention and manager effectiveness across geographies. Returns measured in shipped work, cycle time, and quality rather than…