The Role

You will design, build and run the systems behind our data products, covering ingestion, processing, analytical storage and serving. You own your work from the design document through to production. The people who design a pipeline also run it and answer for its numbers.

Our stack is very wide, and we go deep in every part of it. We don't stop at "it works." We keep measuring, profiling and tuning to make systems faster, cheaper and more reliable. We want engineers who are razor sharp, pay close attention to detail, and care as much about why a query takes 800 ms as whether it returns the right answer.

You don't need blockchain experience. If you're a strong data engineer who wants to learn it, we'd like to hear from you.

What You'll Do

• Design and build scalable, high-throughput data pipelines and platform systems for blockchain data across 30+ chains.

• Build backend data services and loaders in Go and Python.

• Design and evolve data models and analytical workloads in ClickHouse, and tune them for sub-second queries.

• Build incremental, idempotent pipelines on Databricks over billions of rows and tens of terabytes.

• Keep improving the scale, reliability, freshness, performance and cost efficiency of the platform.

• Investigate hard performance and reliability problems and see them through to resolution.

• Work closely with the product, analytics and investigations teams that use the data, and turn their needs into platform designs.

• Help set technical direction through architecture discussions, design reviews and code reviews.

• Mentor engineers and help set strong engineering and operational practices.

What You Bring

• 5+ years building and operating production data systems, including ETL and ELT pipelines. At least two of those years should be spent owning systems that other engineers depend on.

• Expert Python and strong SQL. You write typed, tested code, are comfortable with asyncio and concurrency, and have built incremental data models over billions of rows.

• Go for backend services. You have built and run high-throughput services or data loaders in Go in production.

• ClickHouse in production. You know MergeTree schema and sort-key design, materialised views, bulk loads and query tuning.

• Databricks in production. You have worked with PySpark, Delta Lake and Unity Catalog, designed incremental and idempotent tables, and kept an eye on compute cost.

• Large-scale data. You have hands-on experience with pipelines over billions of rows and tens of terabytes. You understand how partitioning, clustering and file sizing affect cost and latency.

• Kafka, Airflow and Kubernetes in production. You understand delivery semantics and idempotent writes, have handled backfills and failures, and have run data workloads with Docker and Helm. Google Cloud is preferred; AWS is fine.

• An operations and communication mindset. You instrument what you build, write runbooks and post-incident reviews, and can write a design document that a reviewer in another time zone can approve.