Vice President of Engineering

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

What the job involves

• 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
• 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

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