Job Description
About Crystal Crystal Intelligence is a blockchain analytics and compliance intelligence company serving exchanges, financial institutions, regulators, and law enforcement across 100+ blockchains. Our customers depend on us for low-latency, high-availability risk and transaction intelligence that powers operational decisions. Crystal is entering the most consequential platform shift in its history: a full migration from our current data architecture to a new, AI-native data pipeline that will define the company's next decade of scale, speed, and product capability. This is the role that owns it. Role summary Crystal'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 Do Own 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 organiz
