Staff Software Engineer - Data Foundation

Dune's mission is to make onchain finance observable. We're the industry standard for onchain data: a blockchain data and intelligence provider that institutions, protocols, and analysts trust to understand the onchain world and the frontiers of finance. We deliver structured datasets, spanning stablecoins, RWAs, tokens, lending, trading, and more, from 130+ chains and counting, to 1,000+ industry leaders including Visa, WisdomTree, FINRA, the IMF, Bloomberg, Standard Chartered, Coinbase, Forbes, and the Financial Times.

We're a tight knit team of ~50 hard working people, spread across Europe and eastern US timezones 🌍️, having an outsized impact on the industry. We take pride in being ambitious and doing world class work while staying humble and lighthearted. We believe in building open, verifiable data that lets individuals and institutions do deep research into ecosystems like Ethereum, Solana, Robinhood and many more.

We're backed by some of the world's best investors. In February 2022, we announced our Series B funding round led by Coatue and Union Square Ventures, an important milestone that let us double down on our mission.

If you like being challenged and want to work together with a brilliant team on an important mission, come join us.

Dune's Vision

https://dune.com/blog/dune-vision

Values and working at Dune

https://dune.com/careers

Data Foundation owns how blockchain data enters Dune, how it is represented and how it’s delivered externally. This is a role that involves thinking about large distributed data systems, recognising traditional scaling patterns and software lifecycle pitfalls, and is ideal for anyone passionate about those topics.

You will be the engineer we hand ambiguous product requirements to, and will be comfortable coming back with a design, a sequence, and a set of tasks the team can pick up as well as building the hardest parts yourself.

• Turn product requirements into technical requirements: model the domain, define the interfaces and the workflows, and navigate trade-offs

• Break large problems into work other engineers can own, and sequence it so we ship something useful early

• Drive high-impact architectural decisions across the data platform, and own components end to end from design through production

• Work across our stack, mainly Go, Kotlin, Rust and Python where they fit, choosing the right tool rather than the familiar one

• Design for correctness and performance under real volume: large-scale ingestion, distributed processing, and data lake storage

• Raise the bar on design reviews, testing, and how the team breaks work down

• Mentor engineers by example, in a fully distributed team

• You are a strong generalist with real computer science fundamentals, and you use them to adapt quickly to new technologies and problem spaces

• You reason from first principles: you make assumptions explicit, identify the constraints and invariants that matter, and derive the solution rather than reaching for a familiar pattern.

• You use computer science fundamentals as practical tools. You have designed and shipped distributed data systems, making their contracts, state transitions, invariants, and failure modes explicit. You know their fault models and scaling weaknesses

• You are comfortable with ambiguity : you will talk to product, to customers, and to the team, then commit to a scope and defend it

• You use AI tools well enough that they have changed how you work, you understand their failure modes and dislike ai-slop.

• You communicate clearly in writing and get the best out of a distributed team

• Experience with data lakes and formats such as Parquet, Iceberg or Deltalog

• Experience at a company where the data is the product