Team: Data · Location: Bangkok (hybrid) · Reports to: Data Team Lead · Type: Full-time
About Tokenomist
Tokenomist (a product of DAOSurv) is the crypto data platform for token supply: vesting and unlock schedules, emissions, allocations, and on-chain supply metrics. We turn messy, fragmented on-chain and market data into clean, reliable, point-in-time-correct datasets that traders, funds, exchanges, and protocols depend on to make decisions. We're a lean, technical team serving millions of users worldwide.
Trusted by traders and institutions across crypto, with paying institutional customers.
The role
We're hiring a senior engineer to take clear ownership of our core data architecture and pipelines. You'll be the primary owner of how data flows through Tokenomist, from ingestion, through modelling and validation, to the datasets and APIs our product and research teams build on.
This is a senior individual-contributor role with technical leadership: you set engineering standards and mentor one mid/junior data engineer, but it is not a people-management position. You'll work closely with the Data Team Lead, product, and backend engineers on a small, high-ownership team where your decisions ship quickly.
Why this role
- Own the core, from day one. You're the primary owner of the data architecture the whole product stands on, a rare scope to hold as a single senior IC.
- Hard problems worth solving. Point-in-time-correct tokenomics data at scale is genuinely difficult, and few people have done it well.
- Short path to production. Small, senior team; your decisions ship fast and are used across the crypto industry.
What you'll do
- Own the architecture of our core data pipelines and warehouse layer: ingestion from on-chain sources, exchange/market APIs, and partner feeds, through to modelled, queryable datasets.
- Design and evolve data models for tokenomics primitives (supply metrics, vesting/unlock schedules, emissions, allocations), treating point-in-time integrity as a first-class requirement.
- Build for reliability at scale: orchestration, reproducible and idempotent backfills, monitoring, and observability.
- Drive data quality end to end: validation frameworks, reconciliation against external sources, and tracking of source confidence, coverage, and freshness.
- Improve database performance and cost across PostgreSQL/RDS and BigQuery: schema and partitioning design, query optimisation, and locking/throughput behaviour.
- Partner with product, research, and backend (NestJS) on how data is exposed via APIs and surfaced in-product.
- Mentor the data-operations engineer, review designs and code, and raise the bar on engineering practice.