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

Senior

Apr 2026

Summary

System design round at Geico for a software engineer role. The whole thing was one long design question about building a temperature metrics pipeline, and they wanted you to cover basically everything end to end.

Questions Asked (1)

Design a system that ingests temperature readings from a large fleet of sensors at high throughput, stores them efficiently, supports range and aggregation queries, rolls up data across time windows, handles real-time threshold alerting, and scales fault-tolerantly.

System DesignTechnical Trade-offsData Modeling

Author's notes

This was a beast of a question and I don't think I paced it well.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: expected sensor count, ingestion rate, query patterns, latency needs, and retention. Then propose a high-level architecture using a scalable ingestion pipeline (e.g., Kafka), a time-series database (e.g., TimescaleDB or InfluxDB) for storage and queries, and a stream processor (e.g., Flink) for real-time alerting and rollups. Discuss trade-offs, fault tolerance, and scaling strategies.

Pro tip: Emphasize data modeling choices early: time-partitioned tables with appropriate indexes and downsampling can make or break performance at scale. Also, mention that alerting should be idempotent and deduplicated to avoid alert storms.

1. Clarify Requirements and Constraints

Ask about scale (sensors, messages/sec), query types (range, aggregation), latency (ingestion, query, alerting), retention, and consistency needs. This shapes all subsequent design decisions.

2. Design Ingestion Pipeline

Propose a distributed message queue (e.g., Kafka) to buffer and decouple producers from consumers. Ensure it can handle high throughput and provide fault tolerance via replication.

3. Choose Storage and Data Model

Select a time-series database (e.g., TimescaleDB, InfluxDB) or a wide-column store (e.g., Cassandra) optimized for writes and time-range queries. Model data with time as primary dimension, and consider partitioning and indexing.

4. Implement Query and Rollup Mechanisms

Support range queries via time-based indexes. For aggregations and rollups, use pre-aggregation (materialized views or continuous aggregates) and stream processing to compute windows (e.g., 1-min, 1-hour) incrementally.

5. Add Real-Time Alerting and Fault Tolerance

Use a stream processor (e.g., Flink, Kafka Streams) to evaluate thresholds in real-time, with stateful operators for windowing. Ensure fault tolerance via checkpointing, replication, and idempotent alert delivery.

Key Points to Mention

Use of a distributed message queue (e.g., Kafka) for high-throughput ingestion and decoupling.
Time-series database selection and data modeling (partitioning by time, indexing on sensor ID and timestamp).
Pre-aggregation and downsampling strategies for efficient rollups and range queries.
Stream processing for real-time alerting with windowing and state management.
Fault tolerance through replication, checkpointing, and exactly-once semantics.
Scalability via horizontal partitioning (sharding) and auto-scaling consumers.

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