Staff Software Engineer

Who you are

• 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

What the job involves

• 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

Benefits

• Salary in the top 25% of industry
• Equity in the top 25% of industry
• Pension + 401(k) Plan
• Flexible Time Off
• Distributed Team
• Focus Time
• Co-Working Travel Allowance
• Global Offsites & Team Meetups
• Stipend for Home Office
• Private Medical Insurance
• Parental, Foster and Adoptive Paid Leave