VP of Engineering
Skills
About the Role
You will own the end-to-end migration from a legacy stack to a new AI-native data pipeline, ensuring no SLA regressions and uninterrupted customer service. You will restore platform foundations by improving latency, stability, and release cadence. You will rebuild engineering management with clear accountability, hire and structure teams, and set standards for predictable delivery. You will build and deploy AI-assisted engineering infrastructure to boost productivity and automation. You will partner with product, GTM, customer success, and finance to translate engineering investments into customer outcomes, communicate trade-offs and progress to executives, and own the engineering budget and hiring plan.
Requirements
- 10+ years engineering experience
- 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 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 and trade-offs for cutover and validation
- Align engineering, product, and customer success on a single migration roadmap
- Restore API and core platform latency to target
- Reduce database load and fix stability regressions
- Raise release velocity to multiple deployments per week
- Lead multi-chain platform integration with predictable timelines and SLAs
- Establish clear accountability across squad leads and engineering managers
- Set standards for engineering management, planning, and people development
- Make hiring, performance, and structural decisions to scale the organization
- Build shared infrastructure for AI-assisted engineering and automation
- Deploy organization-wide AI productivity tools and measure impact
- Partner with business functions to translate engineering investments into outcomes
- Communicate risks, trade-offs, and progress to executives and the board
- Own the engineering budget, hiring plan, and vendor decisions