VP of Engineering
Skills
About the Role
You will own the end-to-end migration of a legacy data architecture to a new AI-native data pipeline, integrating the pipeline into all product surfaces while preserving customer SLAs. You will lead architectural decision-making, sequence service cutovers, and validate parallel runs to avoid downtime. You will restore platform foundations by reducing database load, improving API latency and scalability, and raising release velocity. You will rebuild engineering management by setting accountability, improving planning and delivery practices, and making hiring and performance decisions. You will build shared AI-assisted engineering infrastructure and drive organization-wide AI productivity and automation, while communicating trade-offs, risks, and progress to business leaders.
Requirements
- 10+ years engineering experience, with 5+ years leading platform, data, or infrastructure organizations as VP Engineering, Head of Engineering, or equivalent
- Led at least one major platform migration or large-scale rebuild while maintaining continuous customer service
- Operated low-latency, high-availability distributed systems with multi-tenant SaaS workloads at production scale
- Production experience integrating AI into engineering workflows, including agent-assisted development and AI-driven automation
- Strong product partnership instincts
- Track record of building accountable, high-ownership engineering organizations
- Direct experience in one or more relevant domains: blockchain or crypto, fintech, payments, fraud or risk platforms, regulatory technology, or large-scale data platforms
Responsibilities
- Own the platform migration end-to-end
- Lead integration of the new data pipeline into all products
- Sequence migrations to preserve revenue and customer SLAs
- Drive architectural decisions for legacy-to-new transition
- Align engineering, product, and customer success on a single migration roadmap
- Restore API and core platform latency targets
- Reduce database load and fix stability regressions
- Increase release velocity to multiple deployments per week
- Lead multi-chain platform integration across 100+ chains
- Establish accountability across squad leads, engineering managers, and platform teams
- Set standards for engineering management and people development
- Make hiring, performance, and structural decisions
- Build shared infrastructure for AI-assisted engineering
- Implement organization-wide AI productivity tools and automation
- Reduce operational expenses through architecture and automation
- Translate engineering investments into customer outcomes and revenue
- Communicate risks and progress to executive team and board
- Own the engineering budget, hiring plan, and vendor decisions