Crystal Intelligence

• Respond promptly and professionally to incoming customer inquiries via email and our internal ticketing system • Triage and categorize support tickets, ensuring complex issues are quickly routed to our L2 and L3 technical teams • Learn the Crystal blockchain analytics platform inside and out to assist users with platform navigation and basic questions • Dedicate regular time to internal training, studying, and shadowing to build the technical skills required for your future promotion • Maintain accurate records of customer interactions and clearly document all initial troubleshooting steps.

• Respond promptly and professionally to incoming customer inquiries via email and our internal ticketing system • Triage and categorize support tickets, ensuring complex issues are quickly routed to our L2 and L3 technical teams • Learn the Crystal blockchain analytics platform inside and out to assist users with platform navigation and basic questions • Dedicate regular time to internal training, studying, and shadowing to build the technical skills required for your future promotion • Maintain accurate records of customer interactions and clearly document all initial troubleshooting steps

• Design and evolve data pipeline architecture and storage systems, driving technical direction across the team; • Build and deliver high-quality data pipelines end-to-end, from initial design through production deployment; • Mentor engineers, raise the technical bar through code reviews, and unblock teammates on complex problems; • Own team outcomes — set expectations, ensure delivery quality, and take accountability for results.

• Owning the transaction monitoring part of the Crystal platform; • Improving stability and observability of the owned services; • Maintaining code to high-quality standards; • End-to-end feature design and implementation; • Coding, debugging, testing and delivering features and improvements in a continuous manner; • Providing code reviews, assistance and constructive feedback to other team members; • Performance optimizations (including database queries optimizations); • Refactoring code as necessary.

• Performance improvements, database query optimisation, and scalability enhancements; • Monitoring and observability configuration and best practices; • Managing and developing internal libraries; • Development pipeline improvements, like code coverage tooling integration, in close collaboration with the DevOps team; • Maintaining code to high-quality standards; • End-to-end feature design and implementation; • Coding, debugging, testing and delivering features and improvements in a continuous manner; • Providing code reviews, assistance and constructive feedback to other team members; • Refactoring code as necessary.

• 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: 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: 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 • 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: 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: 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