Radar's data platform stack uses TypeScript, Rust, and Python. Role focuses on building and scaling the data infrastructure powering 1B+ API calls per day.
Async Production Systems
required2,411 repos of evidenceInfrastructure roles assume backend fluency — the corpus's infra projects are async services first, distributed systems second.
- operate (not just build) long-running async services
- write code that assumes the network will fail
- debug a task leak or a stuck future
Long-running daemons operated by real organizations — read their shutdown, retry, and failure paths.
If you haven't: complete the Backend path's API project — it's the substrate everything below runs on.
Networking Under Failure
required1,092 repos of evidenceEvery distributed failure is a network failure first. The corpus's proxies, DNS servers, and transports are where those failures live in readable code.
- understand network failures: partitions, timeouts, retries, backpressure
- understand what proxies and load balancers actually do to your traffic
- reason about latency budgets across hops
Production proxies and mesh dataplanes — the code your packets already flow through.
Build a small L7 reverse proxy: health checks, per-backend circuit breaking, and a chaos script that kills backends while a load test runs.
Coordination & Consensus
required64 repos of evidenceThe corpus puts consensus in code you can read — implementations, coordination clients, and the databases built on them. Theory is table stakes; these repos are the source.
- understand consensus: what Raft guarantees and what breaks it
- understand leader election, membership, and split brain
- understand why exactly-once delivery is a lie and what to do instead
Raft and consensus libraries themselves — read the state machine, not the blog post about it.
Build a replicated key-value store on a Raft crate: three nodes, leader failover test, and a README explaining exactly what is and isn't guaranteed.
Queues & Streaming
required176 repos of evidenceQueues are how distributed systems absorb failure. The corpus's stream processors, log shippers, and messaging clients show the semantics in running code.
- understand queues: delivery guarantees, visibility timeouts, dead letters
- understand streams versus queues and when each fits
- design consumers that survive redelivery and reordering
The engines that transform data in motion — windowing, watermarks, and exactly-once claims to audit.
Build a work-queue system with at-least-once delivery, visibility timeouts, and a chaos test that kills workers mid-job.
Storage Tradeoffs
recommended441 repos of evidenceRust's storage-engine corpus is unusually strong — reading these codebases is the fastest route to credible storage conversations.
- understand storage tradeoffs: LSM vs B-tree, WALs, compaction
- know what fsync guarantees and what durability actually costs
- work against object storage semantics without pretending it's a filesystem
B-tree and LSM engines you can read end to end — the WAL, the compactor, the recovery path.
Write a tiny persistent KV store: WAL, crash-recovery test, and a README explaining your durability guarantees honestly.
Observability & Reliability
required465 repos of evidenceInfra engineers are judged on behavior under failure. The corpus's reliability layers — tracing, otel, governor — are the tools of that judgment.
- propagate trace context across service boundaries
- understand SLOs, error budgets, and load shedding
- run async systems in production and learn from their incidents
OpenTelemetry-instrumented systems and the collectors themselves — context propagation done for real.
Add distributed tracing across two services with OpenTelemetry — include the cross-service trace screenshot and an SLO doc.
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Backend stack at Radar is TypeScript, Rust, Python, and Scala. Role owns server-side features and pipelines powering the location platform.
Main server language at Radar is TypeScript and Rust. SRE role owns reliability, observability, and infrastructure for the location platform.
ML stack at Radar includes Python, Rust, Airflow, and Spark. Role focuses on machine learning models and data pipelines for geospatial intelligence.