Senior Manager, Engineering-Clustering & Exposure

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  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.

• 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 the team.
• Champion a culture of quality, correctness, and continuous improvement across mission-critical data systems.

• Strong hands-on software and data engineering skills – you're comfortable reviewing code, writing design docs, debugging production systems, and making architectural decisions alongside your team.
• Deep experience with large-scale data processing and distributed systems (e.g., Spark, Flink, Databricks, Airflow, or similar). You can evaluate trade-offs, not just delegate them.
• A track record of leading multiple high-performing engineering teams – while staying close enough to the technical details to unblock, challenge, and elevate the work.
• A keen interest and desire to drive the teams to embrace and thrive in an AI first environment.
• Experience managing across multiple problem domains simultaneously, with the ability to context-switch between strategic planning and hands‑on technical contribution.
• A collaborative, leadership style grounded in technical credibility – your team trusts your judgment because you've been in the trenches with them.
• Excitement about working on large-scale graph, clustering, and real-time data tracing problems, and a genuine desire to own mission-critical infrastructure.
• Strong focus on system reliability, data correctness, and cost-conscious engineering at scale.
• Interest in blockchain and cryptocurrencies (or eagerness to learn – we'll help).

• Hands‑on experience with blockchain technology, cryptocurrency protocols, or on‑chain data analysis.
• Experience with entity resolution, graph algorithms, or large-scale clustering problems.
• Familiarity with cost optimization for high-throughput data pipelines.

• AWS serverless architectures
• Kubernetes
• Spark
• Flink
• Java
• Python
• Databricks
• R…