Crystal Intelligence

Engineering Manager

• 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

VP of Engineering

• 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.

• Own the platform migration end-to-end
• Lead the integration of the new data pipeline into all Crystal products
• Sequence the migration to preserve revenue and customer trust
• Drive architectural decisions and trade-offs
• Hold engineering, product, and customer success aligned on a single migration roadmap
• Restore platform foundations including latency and reliability
• Rebuild the engineering management layer
• Drive AI into engineering as a productivity lever
• Partner with the business to translate engineering investments into outcomes
• Own the engineering budget, hiring plan, and vendor decisions