Role summaryCrystal's engineering organization has grown organically. The current architecture serves a large and loyal customer base, but it is reaching the limits of what feature-driven growth can sustain. In parallel, we have built a new data pipeline architecture led by a dedicated platform team.
The strategic priority for 2026 is to migrate Crystal end-to-end from the legacy stack to the new pipeline - without disrupting customer SLAs, while continuing to ship the product roadmap, and while rebuilding engineering management discipline. The VP of Engineering will own this migration.
The mission is concrete: deliver the new pipeline into production behind every Crystal product, restore platform-grade latency and reliability, and convert the existing organization into one that ships predictably and uses AI as a productivity multiplier.
What You'll DoOwn the platform migration end-to-end- Lead the integration of the new data pipeline into all Crystal products: Crystal Expert, Crystal Foresight, Monitor, Risk Check API, Data Intelligence, and Crystal Light
- Sequence the migration to preserve revenue and customer trust: no SLA regressions, no rollback drama, no surprise downtime
- Drive the architectural decisions and trade-offs that the legacy-to-new transition requires, including data model alignment, service-by-service cutover, and parallel-run validation
- Hold engineering, product, and customer success aligned on a single migration roadmap with clear customer-impact gates
Restore platform foundations- Bring API and core platform latency back to target: 1,000 RPS at sub-two-second latency, scaling toward 10k RPS
- Reduce database load, fix stability regressions exposed by recent releases, raise release velocity to multiple deployments per week
- Lead the multi-chain platform with discipline across 100+ chains: predictable integration timelines, accountable squad ownership, clear SLAs to commercial partners
Rebuild the engineering management layer- Partner with the existing engineering leadership to establish clear accountability across squad leads, engineering managers, and platform teams
- Set the standard for what good engineering management looks like at Crystal: predictable delivery, transparent planning, technical depth, people development
- Make the hiring, performance, and structural decisions required to bring the organization to the level the platform demands
Drive AI into engineering as a productivity lever- Build shared infrastructure for AI-assisted engineering: code generation, automated testing, agent-based migration tooling, internal knowledge systems
- Move Crystal from individual AI tool usage to organization-wide AI productivity, with measurable impact on delivery throughput
- Reduce OpEx-to-revenue through architectural improvements, automation, and reduction of manual operational load
Partner with the business- Work directly with product, GTM, customer success, and finance to translate engineering investments into customer outcomes and revenue
- Communicate trade-offs, risks, and progress clearly to the executive team and board
- Own the engineering budget, hiring plan, and vendor decisions
What Success Looks Like (12 Months)- New data pipeline architecture is in production powering Crystal's core products
- Customer SLAs are met or exceeded throughout the migration; no customer churn attributable to platform instability
- Latency restored and improved; release cadence shifted from monthly to weekly or faster
- Engineering management layer operating with clear accountability and predictable delivery
- AI-assisted engineering infrastructure deployed and measurable productivity gains realized
- OpEx-to-revenue ratio meaningfully reduced toward target
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, with continuous customer service maintained throughout
- 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 - you have shaped what gets built and how it ships
- 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
Department Software Engineering Locations San Francisco Remote status Fully Remote
