Capital One·Software Engineer·Onsite - System Design / Architecture·Intermediate

Intermediate

Jun 2026

Summary

Capital One SWE interview that was basically one big system design prompt about a smart electric meter platform for an apartment building. Not the most complex thing I've ever seen but there's more to unpack than it looks like at first.

Questions Asked (1)

Design a smart electric meter software system for an apartment building, with separate dashboards for tenants and the building owner, displaying electricity usage broken down by unit and floor.

System DesignData ModelingTechnical Trade-offs

Author's notes

I started with the two user types and tried to map out what each one actually needs.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale (number of units, data granularity, real-time vs. batch). Then propose a high-level architecture with data ingestion, storage, and APIs, and dive into data modeling for unit/floor aggregation and dashboard design. Finally, discuss trade-offs around consistency, latency, and cost.

Pro tip: Emphasize multi-tenancy and data isolation: tenants should only see their own data, while the owner sees aggregated views. Also, consider using time-series databases for efficient storage and querying of meter readings.

1. Clarify Requirements

Ask about scale (number of apartments, floors), data granularity (per minute/hour), real-time needs, and security/compliance requirements. Confirm the need for separate dashboards and data access rules.

2. High-Level Architecture

Outline components: smart meters, data collection service (MQTT/HTTP), message queue (Kafka), time-series database (InfluxDB/TimescaleDB), aggregation service, API layer, and frontend dashboards. Consider cloud vs. on-premise.

3. Data Modeling and Aggregation

Design schemas for raw readings (unit_id, timestamp, usage) and aggregated views (per unit, per floor, per building). Plan for efficient queries by time range and entity.

4. Dashboard and API Design

Define REST/GraphQL endpoints for tenants (own usage) and owner (all units/floors). Include authentication/authorization (JWT, RBAC). Sketch dashboard features: real-time usage, historical trends, comparisons.

5. Trade-offs and Scalability

Discuss trade-offs: real-time vs. batch processing, SQL vs. NoSQL, cost of storage vs. query speed. Address scalability (sharding, partitioning) and fault tolerance.

Key Points to Mention

Multi-tenancy and data isolation: tenants access only their data, owner accesses aggregated data.
Time-series data storage and efficient querying for large volumes of meter readings.
Aggregation strategies: pre-compute vs. on-the-fly for floor/unit summaries.
API design with role-based access control (RBAC) and secure authentication.
Real-time vs. batch processing trade-offs for usage updates and alerts.
Scalability considerations: partitioning by building/floor, horizontal scaling of services.

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