Amazon
Mid
Build a distributed in-memory data store in Rust for AWS's serverless big-data/AI storage platform
This SDE II role on AWS Serverless Storage builds core features of a distributed in-memory data store using Rust, focused on high-throughput data processing for big data analytics and AI workloads. You'll also build high-availability microservices to manage cluster lifecycle on EC2/ECS/EKS with dynamic scaling and safe rollouts. Requires 3+ years professional software development, 2+ years design/architecture experience, and hands-on experience with C#/C++/Java/Perl or similar for large-scale multi-tiered/multi-threaded systems.
Step into this interview
Free · a live voice mock calibrated to this exact role
Likely format
Likely includes an online coding assessment plus behavioral rounds structured around Amazon's Leadership Principles, consistent with standard Amazon SDE II hiring loops.
What this interview tests
- Distributed in-memory data store design (Rust: memory safety, concurrency)
- High-throughput data processing for big data / AI workloads
- Cluster lifecycle microservices on EC2/ECS/EKS: scaling, safe rollout
- Node-level performance optimization (CPU/memory/disk)
- API design for flexible, scalable data-persistence layers
- Integration with big-data ecosystem (e.g., Spark)
Common question themes
Design a distributed in-memory data store handling high-volume datasets
Rust concurrency/memory-safety concepts, or equivalent discipline in another systems language
Design a cluster-lifecycle control service with safe rolling updates on EC2/ECS/EKS
A time you optimized CPU/memory/disk utilization at the node level
Object-oriented / multi-threaded system design for a large-scale distributed application
End-to-end ownership of the SDLC for a service you built
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