Blockchain Data Analyst
- Permissionless nature leads to fragmentation of meaning
- Decentralization leads to too many standards
- Immutability leads to exponential data + query infrastructure complexity
- Neutrality means that no one is accountable for interpretations
Blockchain data is public. It is not usable at institutional scale. Despite being open, blockchain data is fragmented, hard to interpret, and painful to maintain. Even a simple question like “Who are the largest Ethereum token holders over time?” can require running nodes, ingesting full chain history, decoding contracts, cleaning edge cases, and writing complex SQL.
Blockchains are optimized for writes, not reads.
They are built for consensus and execution, not searchability, standardization, or financial interpretation. Blockchains are computers, not databases. Every protocol defines its own schema. The same economic action can be encoded in dozens of different ways. The result:
- Fragmented standards
- Exponential complexity
- No accountability for interpretation
- Events without economic meaning
Finance cannot operate on that, it needs an effective system of record.
Allium is building the System of Record for Onchain Finance
Allium ingests, verifies, and standardizes data across 140+ blockchains and 30+ petabytes of history. We close four structural gaps that prevent blockchains from becoming systems of record:
- Semantic Gap: Translating raw events into financial concepts like payments, trades, deposits, and staking income
- Standardization Gap: Mapping thousands of protocols into a single canonical cross chain schema
- Infrastructure Gap: Read optimized, globally distributed data at web scale
- Accountability Gap: Auditable methodology, SLAs, and SOC 1 and 2 compliance
The result is a neutral, canonical data layer institutions can build on with confidence.
Finance is moving onchain
Stablecoins, tokenized assets, trading, staking, and lending are growing rapidly. Institutions need a trusted source of truth for onchain financial activity, just as they rely on Bloomberg or DTCC in traditional markets. Raw blockchains cannot serve that role.
As AI agents begin transacting autonomously, the requirement becomes even stricter. Agents cannot reason over raw event logs. They need structured data, attribution, condition checks, and auditability.
Allium is the read layer that makes onchain finance usable for humans and machines.
Who We Serve
Allium powers three core personas with the same canonical data foundation:
- Finance, Accounting, and Risk TeamsThey need reliable, audit grade answers. They rely on Allium for financial reporting, reconciliation, compliance, risk monitoring, and defensible metrics that can stand up to auditors and regulators.
- Engineers and Product Teams They need low latency, production ready infrastructure. They use Allium to power wallets, trading systems, payment rails, staking infrastructure, and real time applications that cannot break.
- Strategy, Research, and Executive Teams They need clarity and insight. They use Allium to understand ecosystem economics, market structure, user behavior, competitive dynamics, and where capital is flowing onchain.
and of course.. agents ?. Our customers and users include Visa, Stripe, G-SIB Banks, Big 4 Accounting firms, BCG, Coinbase, Phantom, Uniswap and cited by the Federal Reserve.
About the role
At Allium, Blockchain Data Wizards are the data architects who transform raw, fragmented on-chain data into elegant, meaningful abstractions that power the world's largest crypto applications.
You'll be equal parts detective, data engineer, and protocol researcher—diving deep into blockchain internals, reverse engineering DeFi protocols, and crafting data models that institutions and developers rely on.
What you will do- Creating Meaningful Data Abstractions
- Reverse Engineer DeFi Protocol Mechanics
- Indexing New Blockchains
- Building Metrics & Filtering Noise
Design and create metrics people can actually use - volumes, fees, TVL, and user activity, and filter out noise like wash trades, bots, and Sybil attacks. Make it easy for the industry to see what’s real and what matters.
Qualities- Sherlock & Enola Holmes-Level Curiosity
- Product-Minded Data Design
- Comfortable with Ambiguity
- SQL/dbt Mastery
You can write elegant, performant SQL transformations that process large volumes of data. You understand incremental models, macros, and how to build maintainable data pipelines at scale.