🏗️ Architect for Speed & Safety

• Establish organization-wide architectural principles, engineering guardrails, security expectations, and delivery standards that enable multiple teams to move quickly without accumulating unsustainable technical or design debt.
• Govern the use of AI-generated code, designs, and architectural proposals across the organization — ensuring leaders and engineers understand when to trust, challenge, redirect, or reject AI output.
• Drive build-vs-configure-vs-orchestrate decisions at portfolio level, leveraging the internal developer platform, shared services, vendor capabilities, and AI agents appropriately.
• Partner with SaaS vendors and internal platform teams to shape roadmaps, influence product direction, manage escalations, and ensure third-party capabilities are integrated into a coherent product and data architecture.

🔍 Elevate Discovery & Validation

• Model and coach rigorous hypothesis-driven development — ensuring that speed of iteration is matched by quality of learning.
• Design experiments that generate high-signal feedback quickly, and establish the metrics that matter before building begins.
• Challenge assumptions at every stage — including your own.

🛠️ Lead Technical Validation

• Remain hands-on enough to write code, review pull requests, inspect critical technical decisions, validate high-risk AI-assisted outputs, challenge architectural assumptions, and coach leaders on technical quality.
• Maintain deep technical fluency across modern engineering practices, AI-assisted development, cloud-native architecture, security, resiliency, and data/platform patterns — without becoming a bottleneck for every decision.
• Set the engineering excellence bar across the organization, including code review expectations, testing strategy, observability, release governance, platform adoption, and responsible AI usage.
• Institutionalize responsible AI usage across the workforce through standards, training, reusable patterns, measurement, and feedback loops that continuously improve productivity, quality, and employee experience.

🌱 Develop the Team & the Model

• Build a strong leadership bench by coaching managers, product engineering leads, and senior practitioners — sharing mental models, raising standards, and creating clear paths for growth.
• Create a talent enablement strategy for the organization, including capability mapping, skill development, succession planning, AI fluency, career pathways, and targeted hiring plans.
• Continuously evolve the Product Engineer operating model — defining what should be standardized, what should remain autonomous, and how teams learn from each other across the organization.
• Shape cross-functional ways of working with business, product, design, risk, operations, finance, and engineering partners — ensuring the organization is viewed as a strategic partner, not just a delivery function.

What We Are Looking For

Durable Skills — Non-Negotiable

These are the qualities we hire for first. They cannot be taught by a framework or acquired through a certification.

Skill

• Observant
  • You see around corners. You notice when a team is solving the wrong problem, when a metric is measuring the wrong thing, or when a system design will create friction three months from now. You read the room — and the data — before others know there is something to read.
• Curious
  • You have strong opinions, loosely held. You actively seek out perspectives that challenge your mental models and update them when the evidence warrants it. You are as curious about people as you are about technology.
• Determined
  • You drive outcomes, not activity. You know when to push through resistance and when to change direction — and you make that call with clarity and conviction. You do not let ambiguity become an excuse for inaction.

Organizational Leadership — Required

• Demonstrated experience leading a multi-team product engineering organization, including managers, senior practitioners, and cross-functional delivery groups.
• Ability to build high-trust leadership teams, set clear expectations, coach through ambiguity, and hold leaders accountable for outcomes, quality, and team health.
• Experience creating environments where teams can move quickly, challenge assumptions, surface risks early, learn openly, and continuously improve without fear or bureaucracy.
• Comfort operating as an executive-level player-coach: close enough to product and engineering detail to improve decisions, but disciplined enough to scale through leaders and operating mechanisms.
• Proven ability to attract, develop, retain, and enable talent across levels — including succession planning, capability building, performance management, and leadership development.
• Experience managing stakeholders, budgets, capacity plans, hiring plans, vendor/resource mix, and delivery commitments in complex, matrixed organizations.
• Experience overseeing a complex third-party SaaS portfolio, including vendor governance, contract and budget inputs, lifecycle management, platform rationalization, integration strategy, support models, and operational risk.
• Proven ability to lead enterprise or organization-wide transformation programs, particularly those requiring behavioral change, workforce enablement, operating model change, and measurable adoption of AI-enabled ways of working.

Technical Fluency — Required

• Demonstrated ability and willingness to remain hands-on with coding, prototyping, debugging, and technical validation — using direct engineering contribution to improve decisions, not replace team ownership.
• Ability to critically review and validate AI-generated code across multiple languages — identifying correctness issues, security vulnerabilities, and architectural anti-patterns.
• Strong understanding of system design principles: scalability, resilience, data consistency, API design, and security boundaries.
• Proficiency with AI coding tools (Devin AI, Claude Code, GitHub Copilot