Senior Data Engineer

⚡️ About Merkle Science Merkle Science provides blockchain transaction monitoring and intelligence solutions for web3 companies, digital asset service providers, financial institutions, law enforcement and government agencies to detect, investigate and prevent illicit use of cryptocurrencies. Our vision is to make cryptocurrencies safe and provide infrastructure for the safe and compliant growth of cryptocurrencies. Merkle Science is headquartered in New York with offices in Singapore, Bangalore and London. The team has combined experience across Bank of America, Paypal, Luno, Thomson Reuters and Amazon. The company has raised over US$27 MM from SIG, Beco, Republic, DCG, Kenetic, GGV and several others. The Role You will design, build and run the systems behind our data products, covering ingestion, processing, analytical storage and serving. You own your work from the design document through to production. The people who design a pipeline also run it and answer for its numbers. Our stack is very wide, and we go deep in every part of it. We don't stop at "it works." We keep measuring, profiling and tuning to make systems faster, cheaper and more reliable. We want engineers who are razor sharp, pay close attention to detail, and care as much about why a query takes 800 ms as whether it returns the right answer. You don't need blockchain experience. If you're a strong data engineer who wants to learn it, we'd like to hear from you. What You'll Do Design and build scalable, high-throughput data pipelines and platform systems for blockchain data across 30+ chains. Build backend data services and loaders in Go and Python. Design and evolve data models and analytical workloads in ClickHouse, and tune them for sub-second queries. Build incremental, idempotent pipelines on Databricks over billions of rows and tens of terabytes. Keep improving the scale, reliability, freshness, performance and cost efficiency of the platform. Investigate hard performance and reliability problems and see them through to resolution. Work closely with the product, analytics and investigations teams that use the data, and turn their needs into platform designs. Help set technical direction through architecture discussions, design reviews and code reviews. Mentor engineers and help set strong engineering and operational practices.