Google·Software Engineer·Technical Phone Screen·Senior

Senior

Jul 2026

Summary

Google SWE screening focused on system design, specifically building an anomaly detection system. Pretty technical for a phone screen, felt more like a compressed onsite design round.

Questions Asked (1)

Design an anomaly detection system. Walk through the architecture, how you'd identify anomalies in a stream of data, and how the system scales.

System DesignTechnical Trade-offs

Author's notes

This is a meaty question for a screening round.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a high-level architecture that separates data ingestion, processing, and storage. Focus on the streaming anomaly detection approach, explaining algorithms and trade-offs, and finally discuss scalability and fault tolerance.

Pro tip: Emphasize the importance of defining what constitutes an 'anomaly' upfront, as it varies by domain and impacts algorithm choice. Also, discuss how you would handle false positives and concept drift, showing awareness of real-world challenges.

1. Clarify Requirements

Ask questions to understand data volume, velocity, variety, latency requirements, and what defines an anomaly. Determine if the system needs real-time detection or can tolerate batch processing.

2. High-Level Architecture

Outline components: data ingestion (e.g., Kafka, Pub/Sub), stream processing (e.g., Flink, Beam), anomaly detection logic, storage (e.g., Bigtable, BigQuery), and alerting. Discuss data flow and integration points.

3. Anomaly Detection Techniques

Describe algorithms suitable for streaming data, such as statistical methods (z-score, moving average), machine learning models (autoencoders, isolation forests), or rule-based systems. Explain how to train and update models in real-time.

4. Scalability and Fault Tolerance

Explain how to scale horizontally using partitioned streams, distributed processing, and load balancing. Discuss handling failures with replication, checkpointing, and exactly-once semantics.

5. Evaluation and Iteration

Mention metrics for success (precision, recall, latency), monitoring the system, and incorporating feedback to reduce false positives and adapt to changing patterns.

Key Points to Mention

Trade-offs between different anomaly detection algorithms (e.g., supervised vs unsupervised, online vs batch).
Handling concept drift and evolving data distributions.
Use of windowing and state management in stream processing.
Scalability considerations: partitioning, parallelism, and backpressure.
Fault tolerance and data consistency guarantees.
Integration with alerting and monitoring systems (e.g., Prometheus, Grafana).

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