Senior Manager, Engineering - Clustering & Exposure
Chainalysis is the blockchain data platform.
About Chainalysis
Chainalysis offers a blockchain data platform that utilizes sophisticated machine learning and proprietary architecture to handle extensive clustering heuristics, ingest data at scale, and ensure high data accuracy. The platform supports new blockchains and standard tokens automatically, providing comprehensive industry coverage. It simplifies tracing fund flows through complex transactions like bridges, mixers, and DEX swaps. The data provided by Chainalysis is court-admissible and has been instrumental in legal actions. The company provides global, 24/7 support with localized guidance and expertise in various threat typologies and advanced investigative techniques. Through its R&D initiative, Chainalysis Labs, the company continues to innovate and introduce new, unique features and capabilities in blockchain intelligence.
Skills
About the Role
You will build, lead, and mentor two high-performing engineering teams spanning clustering infrastructure and exposure data systems. You will drive the technical vision and roadmap across both domains, balancing reliability, scalability, cost efficiency, and speed of iteration. You will own the re-architecture of exposure systems to trace funds in real time, combating nation-state actors and darknet markets. You will evolve the next-generation clustering platform, ensuring the company stays ahead of an ever-changing blockchain landscape. You will partner with data producers, consumers, and product stakeholders to deliver a world-class developer and data science experience. You will establish engineering processes, tooling, and best practices that accelerate your teams and the broader organization. You will dive into hard problems hands-on when needed, including architecture decisions, incident response, or unblocking the team. You will champion a culture of quality, correctness, and continuous improvement across mission-critical data systems.
Requirements
- Strong hands-on software and data engineering skills - comfortable reviewing code, writing design docs, debugging production systems, and making architectural decisions
- Deep experience with large-scale data processing and distributed systems (e.g., Spark, Flink, Databricks, Airflow, or similar)
- A track record of leading multiple high-performing engineering teams while staying close to technical details
- A keen interest and desire to drive teams to embrace and thrive in an AI first environment
- Experience managing across multiple problem domains simultaneously, with ability to context-switch between strategic planning and hands-on technical contribution
- A collaborative, leadership style grounded in technical credibility
- Excitement about working on large-scale graph, clustering, and real-time data tracing problems
- Strong focus on system reliability, data correctness, and cost-conscious engineering at scale
- Interest in blockchain and cryptocurrencies (or eagerness to learn)
- Nice to have: hands-on experience with blockchain technology, cryptocurrency protocols, or on-chain data analysis
- Nice to have: experience with entity resolution, graph algorithms, or large-scale clustering problems
- Nice to have: familiarity with cost optimization for high-throughput data pipelines
Responsibilities
- 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
- Evolve the next-generation clustering platform
- 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
- 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