About the role
The engineering team at Chainalysis is inspired by solving the hardest technical challenges and building products that establish trust in cryptocurrencies. We're a global organization that thrives on challenging work and doing it alongside exceptionally talented teammates. Our industry evolves rapidly, and our mission is to build a flexible, AI-driven platform that automates entity resolution, optimizes data labeling pipelines, and creates predictive models that identify illicit patterns before they escalate. Our data and solutions have been used to solve some of the world's most high-profile criminal cases and grow consumer access to cryptocurrency safely. Now, by pairing the industry's most trusted blockchain data with agents that reason, investigate, and act, we help our customers scale their workflows as the cryptocurrency economy becomes increasingly mainstream. The Clustering and Exposure teams own two of the most critical datasets at Chainalysis - foundational data that powers every product we offer to national security agencies and global financial institutions. The Clustering team builds the systems that link blockchain addresses to real-world entities at scale. The Exposure team traces the on-chain movement of funds, continuously monitoring how value flows between known and unknown actors across blockchains via swaps and bridges. Together, these teams form the analytical backbone of the company and everything moves fast with a high bar for quality and correctness. In this role, you’ll: Build, lead, and mentor two high-performing engineering teams spanning clustering infrastructure and exposure data systems. Drive the technical vision and roadmap across both domains, balancing reliability, scalability, cost efficiency, and speed of iteration. Own the re-architecture of exposure systems to trace funds in real time, combating nation-state actors and darknet markets. Evolve the next-generation clustering platform, ensuring Chainalysis stays ahead of an ever-changing blockchain landscape. Partner with data producers, consumers, and product stakeholders to deliver a world-class developer and data science experience. Establish engineering processes, tooling, and best practices that accelerate your teams and the broader organization. Dive into hard problems hands-on when needed - architecture decisions, incident response, or unblocking
