First 90 days leading Developer Experience at Vanta: Building trust through better systems

When I joined Vanta in December 2025 to lead Developer Experience, I stepped into a broad mandate spanning build systems, testing infrastructure, CI/CD, deployment workflows, and dependency management. At the same time, the engineering organization was preparing to expand.

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I learned that Vanta’s mission is to help businesses earn and prove trust, and that standard applies directly to how we build software. Trust manifests operationally through reliability, deployment safety, auditability, and engineering discipline. As the organization scales, the design of our internal systems becomes inseparable from the trust we deliver externally.

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The central question is not how to support more engineers, but how to design an engineering system that improves as it grows—while strengthening trust at every step.

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Ninety days in, here are the lessons that shape our approach.

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Developer Experience is systems design

Developer Experience is often framed as tooling—pipelines, internal platforms, automation scripts. In reality, it’s the operating system of an engineering organization.

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It determines:

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• How quickly ideas move from concept to production
• How confidently engineers ship
• How much cognitive load they carry
• How resilient the organization becomes under growth
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At Vanta, Developer Experience sits at the intersection of platform engineering, architecture, reliability, and now AI-first workflows. We build products for internal teams, designing developer workflows with the same rigor and iteration mindset we apply to customer-facing features. That means building with engineering teams to build products that work for them.

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The work isn’t only about polishing edges. It’s about designing leverage. And in a company built on trust, leverage must increase confidence, not trade it for speed.

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Three core insights

1. Developer experience is company-specific

Previously, I led Developer Experience teams at Coinbase, where we built developer productivity systems at a meaningful scale. That experience provided durable frameworks for thinking about platform design, ownership models, and leverage. However, those frameworks don’t transfer directly across companies without some adaptation.

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Every organization operates within a unique set of constraints, including product architecture, deployment topology, risk tolerance, regulatory posture, and team composition. A security and compliance platform operates under different assumptions than a fintech company, and those differences materially shape developer workflows. While the underlying principles of effective Developer Experience are portable, their implementation must reflect the company’s specific operating environment.

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Developer Experience and rest of company platforms need to define engineering goals. As a result, the Developer Experience team had become a default escalation path for a wide range of developer friction, including systems outside our direct control. This ambiguity diluted accountability, slowed decision-making, and weakened trust with our internal stakeholders.

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My first 90 days focused on understanding Vanta’s context and establishing structural clarity: defining end-to-end ownership, distinguishing between enablement and operational responsibility, and formalizing partnership models across infrastructure and product teams.

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Clarifying these boundaries improved predictability and accountability. Engineers had clearer pathways for support and Developer Experience could commit to outcomes within a well-defined scope.

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2. AI changes the bottleneck

At Vanta we're building an AI-first engineering culture. AI meaningfully accelerates code generation, reduces boilerplate, and compresses iteration cycles. Engineers increasingly spend less time writing every line of code and more time orchestrating systems, reviewing generated output, and refining intent.

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As velocity increases, the primary constraint shifts from generation to validation. In AI-assisted development, the central risk is no longer syntax errors but misplaced confidence. Code that appears correct and passes superficial checks might fail to handle edge cases, scale appropriately, or reflect true system intent.

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Testing strategies designed for environments where humans wrote every line must evolve when AI generates significant portions of the system. Validation mechanisms must become more rigorous, automated, and structurally embedded in the development lifecycle.

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If AI reduces the cost of creating software, Developer Experience must reduce the cost and increase the reliability of validating software at scale.

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In practice, this requires:

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• Test generation and validation workflows that keep pace with AI-assisted output
• CI systems optimized for high-signal feedback rather than volume
• Automated checks that scale proportionally with code generation
• Observability systems that surface behavioral intent, not just operational metrics
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The craft of engineering is shifting from primarily writing code to systematically ensuring correctness. Developer Experience must evolve in parallel to support that transition.
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3. Developer Experience must operate as a platform

This past year's growth at Vanta shows that the Developer Experience can't scale proportionally with headcount. To remain effective, Developer Experience must operate as a platform rather than a service desk. The goal is to build systems that scale more efficiently than the organization itself.

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Platform leverage, in this context, means investing in foundational capabilities such as:

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• Self-service infrastructure that minimizes cross-team dependencies
• CI/CD systems designed to preserve developer flow and provide reliable feedback
• Dependency management practices that prevent configuration drift
• Tooling that reduces cognitive load rather than introducing additional operational overhead
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The role of a Developer Experience engineer is not simply to resolve individual issues, but to design systems that eliminate recurring categories of issues entirely. This involves encoding best practices into workflows, converting implicit knowledge into explicit defaults, and creating paved paths that make the correct approach the easiest one.

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Growing engineering effectively requires reducing systemic friction as the organization grows. In a company whose product is built around trust, unmanaged complexity introduces operational risk, and operational risk carries real cost.

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The road ahead

We want to unlock the high leverage high agency engineering experience while maintaining both velocity and trust. Achieving that requires deliberate investment in systems that scale predictably as the organization expands.

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The next phase of our work centers on three priorities:
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1. AI-native workflows: Designing test generation, validation, and debugging systems that keep pace with AI-assisted development and ensure confidence scales with output.
2. Platform leverage: Building self-service infrastructure and CI/CD capabilities that reduce dependency chains and preserve developer flow as the organization grows.
3. Trust at scale: Embedding guardrails and observability into the development lifecycle so that safe, reliable shipping remains the default—even as complexity increases.
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Developer Experience, in this context, is foundational infrastructure. It shapes how engineers build, how confidently they ship, and how consistently we uphold the standards our customers rely on.

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We’re continuing to grow the team with engineers who think in systems, care deeply about correctness, and want to help define what high-trust, AI-first development looks like in practice.

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If this resonates, let’s connect.