Onchain Analytics Engineer

Join Token Terminal as an Onchain Analytics Engineer to transform raw blockchain data into standardized financial metrics. Work remotely in a small team focused on DeFi protocols and data quality. Token Terminal's mission is to become the ground truth for onchain data. Founded in 2020, we transform raw blockchain data into standardized, comparable financial and usage metrics across 100+ blockchains, 1,200+ protocols, and 3,000+ tokenized assets. Our data pipeline manages 2+ PB of onchain data, processes approximately 200 TB, and runs ~30,000 dbt models every day.

• •Analyze DeFi protocols end-to-end: read smart contracts, map fee structures and token flows, and write the SQL that turns decoded onchain events into standardized financial metrics
• •Build and maintain dbt models across our four-layer data pipeline, from decoded contract data through to production metrics
• •Develop chain-level data infrastructure: normalized transaction tables, price feeds, and token transfer abstractions across 100+ blockchains
• •Investigate and resolve data issues by tracing metrics back through the pipeline to the individual block and transaction that caused them
• •Write and maintain methodology documentation. Our methodology is public, auditable, and part of the product.
• •Support commercial and product teams with data analysis, custom datasets, and integration QA

• •Competitive salary and equity
• •Employee-friendly equity terms with extended exercise windows
• •20 days paid vacation + local public holidays
• •Remote-first, no required commute

• •2+ years working with SQL in a production analytics or data engineering environment
• •Understanding of how DeFi protocols work and generate revenue. You know the difference between a swap fee, a borrow rate, and a validator reward.
• •Ability to read smart contracts well enough to understand what events mean and where value flows
• •Clear written communication. You will explain methodology to users who manage institutional portfolios.
• •Curiosity about protocols. You read docs and contracts before being told what to look at.
• •Comfortable with ambiguity in data. Onchain data is messy. There is no clean schema handed to you.
• •Track record of owning data quality problems end-to-end, not just writing queries
• •Self-directed in a small, remote team without heavy process overhead