About the company
Allium makes blockchain data accurate, simple and fast Blockchain data is hard, messy, and chaotic When we started out in late 2021 our thesis was simple - blockchain data, despite it being public and free, was difficult to understand, clunky to access and troublesome to maintain. Answering a simple question like “Who are the biggest Ethereum token holders over time?” requires an engineering team to run their own RPC nodes, ingest the full history of the blockchain, clean the data, transform the data and finally summon a wizard to cast a complex SQL query. Accessing data is hard because blockchains are optimized for Writes and not Reads Why is it so hard? Blockchains have historically been optimized for Writes (getting data onto the blockchain) and less for Reads (getting data OUT of the blockchain). This is because optimization efforts were focused on increasing transaction throughput and building fault tolerant and scalable consensus algorithms. This neglect makes it hard to get data out efficiently and reliably at scale.
Job Summary
What you will do
📍Creating Meaningful Data Abstractions Transform messy on-chain data into unified, intuitive data models (e.g dex.trades, lending.liquidations) that work across verticals and protocols, providing users with granular, reliable insights. 📍Reverse Engineer DeFi Protocol Mechanics Dive deep into the mechanics of new DeFi protocols, dissect smart contract interactions and on-chain events, extract the data that matters, and transform it into clean, production-ready models using SQL/dbt. 📍Indexing New Blockchains Dive into a blockchain’s raw data structures, ensure complete and accurate data capture, and design schemas that translate native blockchain data into reliable internal models, becoming the trusted source of truth for that ecosystem. 📍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 📍You find peculiarities in data that others miss. You question assumptions, dig deeper when something looks off, and help the industry redefine narratives with evidence-based insights. 📍Product-Minded Data Design 📍You don't just build tables—you design data products. You think about query patterns, performance, documentation, and how end users will interact with your models. 📍Comfortable with Ambiguity 📍New blockchains and protocols have sparse documentation. You thrive when you need to figure things out yourself through exploration and experimentation. 📍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.
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