SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Dune Analytics is the industry standard for onchain data, providing blockchain data and intelligence to 1,000+ industry leaders including Visa, WisdomTree, FINRA, the IMF, Bloomberg, Standard Chartered, Coinbase, Forbes, and the Financial Times. The company delivers structured datasets spanning stablecoins, tokens, lending, trading, and payments from 130+ chains to institutions, protocols, and analysts worldwide.
The Data Products team builds and owns datasets end-to-end: from raw chain data through decoding to 3,000+ models and 4 petabytes of curated data shared directly with customers and replicated into their warehouses. This Staff Software Engineer role focuses on the lifecycle of building high-quality data systems, treating it as a software architecture problem in a data domain.
You will design and build the control plane for the curated data lifecycle, including dependency-aware orchestration, backfills, restatements, retries, partial failure, and recovery. You'll decide, dataset by dataset, whether the solution is a model, service, or job, and own that architecture through production. You will design contracts between ingestion and curation so datasets can be reasoned about end-to-end, and build alerting and data quality signals that catch real problems while minimizing false positives. You'll work across Go, Kotlin, Rust, Python, and SQL, choosing the right tool for each problem. You'll break large problems into work other engineers can own and sequence delivery so the team ships useful value early.
This is a hybrid backend/data engineering role where you'll be handed ambiguous product requirements and return with a design, sequence, and work the team can execute, while building the hardest parts yourself. You'll work across a distributed team spanning Europe and the eastern US with flexible hours and a remote-first approach.
REQUIREMENTS:
- Backend engineer with deep experience in data systems, or data engineer who became a strong software engineer; must ship production services, not only pipelines
- Built or materially extended orchestration and scheduling systems; can explain precisely what breaks at scale and why
- Handled schema evolution and data correctness in systems with real downstream consumers, where breaking changes have measurable cost
- Built or operated stateful stream processing in production (Flink, Kafka Streams, Spark Structured Streaming, RisingWave, Materialize, Feldera)
- Strong SQL and modeling skills on large datasets; interest in how query engines execute your work
- Solid computer science fundamentals and distributed systems understanding
- Debug independently and drive root cause analysis to lasting fixes
- Use AI tools effectively, understand their failure modes, and dislike low-quality AI output
- Communicate clearly in writing and get the best out of distributed teams
PLUS (not required):
- Deep experience with transformation frameworks (dbt, SQLMesh), especially having hit their limits and built beyond them
- Data lake formats (Parquet, Iceberg, Deltalog)
- Stateful stream processing in production
- Experience at companies where data is the product
