Design Sensor Data Processing With AI-Assisted Implementation
Company: Applied
Role: Software Engineer
Category: System Design
Difficulty: medium
Interview Round: Technical Screen
Design a system that ingests sensor data, processes it, stores it, and supports queries over the processed results. In the coding round, the candidate could use an AI coding tool to implement the project from a README. In the system design round, the focus shifted to how the data should be stored and how the system should scale.
<details>
<summary>Hint 1</summary>
Start by naming the core entities, constraints, and success criteria.
</details>
<details>
<summary>Hint 2</summary>
Make the trade-offs explicit before going deep on implementation details.
</details>
### Constraints & Assumptions
- Sensor data arrives continuously or in batches from many devices.
- Queries need processed results, not only raw records.
- The implementation should be understandable enough to demo and debug.
- AI assistance is allowed, but the candidate remains responsible for correctness.
### Clarifying Questions to Ask
- What is the sensor event schema?
- What queries must be supported?
- Is data append-only or can readings be corrected?
- What retention and aggregation windows are required?
- What latency is expected from ingest to queryability?
### What a Strong Answer Covers
```premium-lock What a Strong Answer Covers
```
### Follow-up Questions
- How would you handle duplicate sensor readings?
- How would the design change for one million devices?
- What data would you pre-aggregate?
- How would you test an AI-generated implementation under time pressure?
Quick Answer: Prepare for a sensor-data processing system design prompt where an AI-assisted coding exercise leads into storage and scaling questions. The interview focuses on raw versus processed data, schema design, time-range queries, aggregation, validation, and productionizing generated code.