VP of Engineering: Platform Migration & AI Transformation

About Crystal

Crystal Intelligence is a leader in blockchain analytics and compliance, providing essential risk and transaction intelligence to exchanges, financial institutions, regulators, and law enforcement across over 100 blockchains. Our customers rely on our low-latency, high-availability solutions for critical operational decisions.

We are at a pivotal moment, embarking on a full migration from our current data architecture to a new, AI-native data pipeline. This strategic shift is designed to define Crystal's next decade of scale, speed, and product innovation, and this role is central to its success.

Role Summary

Crystal's engineering organization has evolved organically. While our current architecture serves a large customer base, it's reaching its growth limits. Concurrently, we've developed a new data pipeline architecture championed by a dedicated platform team.

The primary strategic goal for 2026 is to migrate Crystal end-to-end from the legacy stack to this new pipeline. This migration must be executed without disrupting customer Service Level Agreements (SLAs), while continuing to deliver on the product roadmap, and simultaneously rebuilding engineering management discipline. The VP of Engineering will spearhead this critical migration.

Mission: Deliver the new pipeline into production for all Crystal products, restore platform-grade latency and reliability, and transform the engineering organization into one that ships predictably and leverages AI as a significant 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.
• Strategically sequence the migration to preserve revenue and customer trust, ensuring no SLA regressions, avoiding rollback scenarios, and preventing surprise downtime.
• Drive critical architectural decisions and trade-offs inherent in the legacy-to-new transition, including data model alignment, service-by-service cutovers, and parallel-run validation.
• Align engineering, product, and customer success teams around a unified migration roadmap with clearly defined customer-impact gates.

Restore Platform Foundations

• Re-establish target API and core platform latency: achieve 1,000 Requests Per Second (RPS) at sub-two-second latency, scaling towards 10k RPS.
• Reduce database load, resolve stability regressions from recent releases, and increase release velocity to multiple deployments per week.
• Lead the multi-chain platform (supporting 100+ chains) with discipline, ensuring predictable integration timelines, accountable squad ownership, and clear SLAs for commercial partners.

Rebuild the Engineering Management Layer

• Collaborate with existing engineering leadership to establish clear accountability across squad leads, engineering managers, and platform teams.
• Define and implement best practices for engineering management at Crystal, focusing on predictable delivery, transparent planning, technical depth, and people development.
• Make necessary hiring, performance, and structural decisions to elevate the organization to meet the platform's demands.

Drive AI into Engineering as a Productivity Lever

• Develop shared infrastructure for AI-assisted engineering, including code generation, automated testing, agent-based migration tooling, and internal knowledge systems.
• Transition Crystal from individual AI tool usage to organization-wide AI productivity, demonstrating measurable impact on delivery throughput.
• Reduce the operational expense (OpEx)-to-revenue ratio through architectural improvements, automation, and reduction of manual operational load.

Partner with the Business

• Work directly with product, Go-to-Market (GTM), customer success, and finance teams to translate engineering investments into customer outcomes and revenue.
• Clearly communicate trade-offs, risks, and progress to the executive team and board.
• Own the engineering budget, hiring plan, and vendor decisions.

What Success Looks Like (12 Months)

• The new data pipeline architecture is live in production, powering Crystal's core products.
• Customer SLAs are consistently met or exceeded throughout the migration; no customer churn is attributed to platform instability.
• Latency is restored and improved; release cadence shifts from monthly to weekly or faster.
• The engineering management layer operates with clear accountability and predictable delivery.
• AI-assisted engineering infrastructure is deployed, yielding measurable productivity gains.
• The OpEx-to-revenue ratio is meaningfully reduced towards target.

Requirements

• 10+ years of engineering experience, with a minimum of 5 years leading platform, data, or infrastructure organizations as VP Engineering, Head of Engineering, or equivalent.
• Proven experience leading at least one major platform migration or large-scale rebuild, maintaining continuous customer service throughout.
• Experience operating low-latency, high-availability distributed systems supporting 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; demonstrated ability to shape product development and delivery.
• 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.