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

Senior

Jun 2026

Summary

System design round at Robinhood for a software engineer role. The whole thing was centered on one big problem: build a security audit and monitoring system from scratch. Dense topic with a lot of moving parts, and the conversation went pretty deep.

Questions Asked (1)

Design a security audit and monitoring system that captures sensitive operations across company services, supports querying for investigations, detects anomalies, and stores logs in a tamper-evident way without affecting the latency or availability of the services being audited.

System DesignTechnical Trade-offsData Modeling

Author's notes

This is a meaty one.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then design a decoupled, asynchronous pipeline that captures events without impacting service latency. Focus on tamper-evident storage using cryptographic techniques and scalable querying for investigations and anomaly detection.

Pro tip: Emphasize that audit logs should be immutable and append-only, and consider using a write-ahead log or blockchain-inspired hash chain for tamper evidence. Also, discuss how to handle sensitive data with encryption and access controls to maintain compliance.

1. Clarify Requirements and Constraints

Ask about the scale, types of sensitive operations, latency requirements, retention policies, and compliance needs. Understand what 'tamper-evident' means to the interviewer.

2. Design Event Capture and Ingestion

Propose an asynchronous, non-blocking mechanism (e.g., sidecar, library, or message queue) to capture events from services without adding latency. Ensure events are enriched with metadata and sent to a central pipeline.

3. Design Storage and Tamper-Evidence

Choose a storage solution that supports append-only writes, such as a distributed log or database with cryptographic hashing (e.g., hash chain). Discuss encryption at rest and access controls.

4. Design Query and Anomaly Detection

Outline how to index and query logs efficiently for investigations, and how to run real-time or batch anomaly detection using rules or machine learning models.

5. Address Trade-offs and Scalability

Discuss trade-offs between consistency, availability, and latency; how to scale ingestion and storage; and how to handle failures and backpressure.

Key Points to Mention

Asynchronous event capture using message queues (e.g., Kafka) to decouple services from audit system
Tamper-evident storage via hash chains or Merkle trees, with periodic anchoring to an immutable store
Indexing and querying using time-series databases or search engines (e.g., Elasticsearch) for fast investigations
Anomaly detection using streaming analytics (e.g., Flink) or batch processing with ML models
Encryption of sensitive data in transit and at rest, and strict access controls (e.g., RBAC)
Monitoring and alerting on the audit system itself to ensure availability and integrity

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