Centers on system and solution design decisions and the trade offs inherent in architecture choices. Candidates should be able to identify alternatives, clarify constraints such as scale cost and team capability, and articulate trade offs like consistency versus availability, latency versus throughput, simplicity versus extensibility, monolith versus microservices, synchronous versus asynchronous patterns, database selection, caching strategies, and operational complexity. This topic covers methods for quantifying or qualitatively evaluating impacts, prototyping and measuring performance, planning incremental migrations, documenting decisions, and proposing mitigation and monitoring plans to manage risk and maintainability.
HardTechnical
0 practiced
Design a globally unique ID generation solution for a multi-region platform that minimizes coordination on the critical path. Compare approaches such as Twitter Snowflake, UUIDv1, Hi-Lo, and time-sharded sequences, and discuss clock skew, throughput, collision risk, and monotonicity concerns.
HardSystem Design
0 practiced
Architect an anti-entropy and reconciliation mechanism for eventual-consistent data replicated across distributed services where out-of-order and duplicate events can occur. Include detection, reconciliation algorithms (digests, Merkle trees), deterministic conflict resolution policies, and operational tooling for large datasets.
HardTechnical
0 practiced
Data skew causes a Spark job to fail on a few partitions while most tasks finish quickly. Architect system-level solutions: skew detection, salting, map-side pre-aggregation, adaptive query planning, autoscaling executors, and discuss trade-offs in extra shuffles, storage, and developer complexity.
MediumBehavioral
0 practiced
Tell me about a time you had to advocate for a technical trade-off that faced stakeholder resistance. As a data engineer, describe the context, the options you presented, how you quantified trade-offs (cost, latency, complexity), how you persuaded stakeholders, and the outcome.
MediumSystem Design
0 practiced
Design a data ingestion architecture to reliably handle 1 million events per second peak, delivering data into downstream analytics stores with <5s tail latency. Specify high-level components (ingress, durable buffer, partitioning, consumers), buffering strategies, ordering guarantees, and failure/retry approaches in a cloud environment.
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