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Vanta·Software Engineer·Onsite - System Design / Architecture·Intermediate

Intermediate
May 2026Remote

Summary

Did a virtual onsite for a Software Engineer role at Vanta. The system design portion focused on metrics around user activity, which felt pretty open-ended.

Questions Asked (1)

Q1

Design a system that tracks daily and monthly active users.

System DesignProduct Analytics & Metrics
Author's notes

Pretty classic metrics-tracking prompt but the scope can balloon fast if you're not careful.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: what defines an active user, data freshness, scale, and query patterns. Then design a scalable event ingestion pipeline that processes user activity events and maintains daily and monthly active user counts using efficient data structures like HyperLogLog or bitmap indexes. Finally, discuss storage, querying, and trade-offs between accuracy and performance.

Pro tip: Emphasize the importance of defining 'active user' precisely and handling late-arriving data, as these are common pitfalls in production analytics systems. Also, mention how you would validate the system with A/B testing or reconciliation with source data.

1. Clarify Requirements

Ask about scale (events per day, number of users), definition of active user (e.g., any event, specific actions), data freshness (real-time vs batch), and query patterns (ad-hoc vs dashboard).

2. High-Level Design

Outline the main components: event collection (SDK, API), ingestion pipeline (Kafka, Kinesis), processing (stream or batch), storage (OLAP, NoSQL), and serving layer (API, dashboard).

3. Data Modeling & Counting Strategy

Choose data structures for efficient distinct counting: exact methods (sets, bitmaps) for small scale or approximate (HyperLogLog) for large scale. Discuss daily vs monthly aggregation and how to handle month boundaries.

4. Scalability & Reliability

Address partitioning, replication, fault tolerance, and exactly-once processing. Consider using a lambda architecture or kappa architecture for real-time and batch consistency.

5. Trade-offs & Optimizations

Discuss trade-offs between accuracy and memory, latency vs cost, and how to optimize queries (pre-aggregation, indexing). Mention monitoring and alerting.

Key Points to Mention

  • Definition of active user: any event vs specific action, and how it impacts counting.
  • Use of approximate algorithms like HyperLogLog for scalable distinct counting with low memory.
  • Handling late-arriving data and timezone considerations for daily/monthly boundaries.
  • Storage choices: time-series databases, columnar stores (e.g., ClickHouse), or bitmap indexes (e.g., Roaring Bitmap).
  • Real-time vs batch processing: trade-offs and hybrid approaches (Lambda architecture).
  • Data retention and cost: how long to keep raw events vs aggregated counts.

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