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

SeniorPrefer not to say
May 2026

Summary

Vanta system design round, one big question about building a DAU/MAU metrics pipeline at scale. The question had a lot of surface area and I don't think I covered all of it well, but it was a genuinely interesting problem to work through.

Questions Asked (1)

Q1

Design a system that computes and serves DAU and MAU for a large consumer app. Cover metric definitions, event ingestion, deduplication, accurate computation across time zones and late arrivals, storage and compute choices, dashboard serving, and monitoring.

System DesignProduct Analytics & MetricsTechnical Trade-offs
Author's notes

This one sprawls.

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AI HintsAI Generated

Suggested Approach

Start by clarifying metric definitions and requirements (e.g., DAU/MAU definitions, time zones, latency, accuracy). Then walk through the data flow: event ingestion, deduplication, storage, computation, serving, and monitoring. Emphasize trade-offs between accuracy and cost/latency, and propose a scalable architecture using appropriate technologies.

Pro tip: Define DAU/MAU precisely upfront—e.g., 'active' means any event, and a day is based on user's local timezone—and discuss how to handle late data with a lambda architecture or incremental updates. This shows you understand real-world complexities and avoids ambiguity.

1. Clarify Requirements and Definitions

Ask questions to pin down DAU/MAU definitions, time zone handling, acceptable latency, and accuracy requirements. Define what constitutes an 'active' user and how to handle late-arriving events.

2. Design Event Ingestion and Deduplication

Outline a scalable ingestion pipeline (e.g., Kafka) that collects events with user IDs and timestamps. Implement deduplication using unique event IDs or idempotent processing to avoid double-counting.

3. Choose Storage and Compute Strategy

Select storage (e.g., data lake for raw events, OLAP for aggregates) and compute (batch for accuracy, stream for freshness). Discuss partitioning by date and user to enable efficient queries.

4. Compute DAU/MAU with Time Zone and Late Data Handling

Compute daily active users by grouping events by user and local date, then count distinct users. For MAU, aggregate over a rolling 30-day window. Handle late arrivals by reprocessing affected days or using incremental updates.

5. Serve Metrics and Monitor

Expose DAU/MAU via a low-latency API or dashboard, caching results. Monitor data freshness, pipeline health, and metric anomalies to ensure reliability.

Key Points to Mention

  • Precise definition of DAU/MAU: what counts as an active user (e.g., any event, specific actions) and the time window (e.g., calendar day in user's timezone).
  • Deduplication strategies: using unique event IDs, idempotent writes, or exactly-once processing to avoid inflating counts.
  • Time zone handling: store timestamps in UTC, convert to user's local timezone for day boundaries, and consider using a timezone database.
  • Late-arriving data: use a lambda architecture (batch + speed layer) or incremental aggregation with periodic reprocessing to correct counts.
  • Storage and compute choices: e.g., Kafka for ingestion, S3/HDFS for raw data, Druid/ClickHouse/BigQuery for OLAP, Spark/Flink for processing.
  • Serving and monitoring: pre-aggregate metrics for fast queries, cache results, and set up alerts for data delays or anomalies.

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