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NVIDIA·Software Engineer·Onsite - System Design / Architecture·Senior

Senior
May 2026

Summary

System design round at NVIDIA for a software engineering role. The question was a single sprawling design problem that covered a lot of ground, and I felt like I kept getting pulled in different directions trying to cover everything.

Questions Asked (1)

Q1

Design a bidirectional data synchronization platform that ingests planned maintenance and outage events from cloud providers like AWS, GCP, and Azure, normalizes them into a shared schema, and surfaces them on an internal dashboard. You should also cover how updates flow back to providers, how you ensure durability and idempotency, how fresh the dashboard stays, and what your observability story looks like.

System DesignAPI & IntegrationsData Modeling
Author's notes

This one is massive.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a high-level architecture that decouples ingestion, normalization, and synchronization using an event-driven pipeline with a canonical schema. Detail the bidirectional sync with idempotent operations, durability via persistent queues and exactly-once semantics, freshness through near-real-time processing, and observability with metrics, logs, and traces.

Pro tip: Emphasize idempotency and conflict resolution strategies (e.g., versioning, timestamps) to handle duplicate or out-of-order events, and discuss how you'd test the system with chaos engineering to ensure resilience.

1. Clarify Requirements and Constraints

Ask about scale (events per second, number of providers), latency requirements, consistency needs, and failure modes. Confirm the need for bidirectional sync and any compliance or security constraints.

2. Design High-Level Architecture

Propose an event-driven pipeline: ingestion adapters per provider, a message queue (e.g., Kafka) for durability, a normalization service to map to a canonical schema, and a sync service for outbound updates. Include a database for the normalized events and a dashboard for visualization.

3. Detail Data Flow and Synchronization

Explain how events are ingested (polling vs. webhooks), normalized, stored, and pushed to the dashboard. For outbound sync, describe how updates are sent back to providers, ensuring idempotency with unique event IDs and deduplication, and handling conflicts with versioning or last-write-wins.

4. Address Durability, Idempotency, and Freshness

Discuss using persistent queues, write-ahead logs, and transactional writes for durability. Ensure idempotency via idempotency keys and deduplication windows. For freshness, use stream processing (e.g., Kafka Streams, Flink) to update the dashboard in near-real-time, with caching and incremental updates.

5. Define Observability and Monitoring

Outline metrics (ingestion rate, latency, error rates), logging (structured logs with correlation IDs), tracing (distributed tracing across services), and alerting. Include dashboards for system health and data quality checks.

Key Points to Mention

  • Canonical schema design and normalization techniques (e.g., Avro, Protobuf) to handle diverse provider formats.
  • Idempotency implementation using unique event IDs, deduplication, and exactly-once processing semantics.
  • Durability through persistent message queues (Kafka), replication, and transactional writes.
  • Freshness via stream processing and incremental updates to the dashboard, with SLAs for latency.
  • Bidirectional sync challenges: conflict resolution, rate limiting, and API versioning for provider APIs.
  • Observability: metrics (Prometheus), logging (ELK), tracing (Jaeger), and alerting (PagerDuty) with data quality monitoring.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.