# Design a data quality monitoring platform: system design interview question

> Data checks must respect source cost limits while late data, source changes, and seasonal patterns affect result accuracy.

Source: https://sysround.com/questions/design-data-quality-monitoring-platform
Updated: 2026-11-05


Difficulty: hard. Levels: Senior, Staff+.

## How the interviewer opens it

"Design a platform that detects missing, stale, malformed, or unusual data across analytical datasets. Keep its cost to production systems within agreed limits."

## What to clarify first

- Dataset scale
- Check scale and frequency
- Source access and snapshots
- Check meaning
- Latency
- Source cost limits
- Read and write mix
- Consistency, availability, and durability
- Anomalies and alerts
- Scope limits

## Scope of a strong answer

Core requirements:

- Define checks and expected arrival times for datasets.
- Evaluate checks within source cost limits.
- Track results, coverage, and metric history.
- Send alerts for data failures and check failures.

Usually out of scope:

- Repair data or change source pipelines.
- Execute arbitrary user code.
- Scan transactional databases.
- Provide a general data catalog.

## What interviewers probe

### Cost on source systems

Checks share data, but their combined source work must remain below a hard limit despite wrong estimates, failures, and concurrent tenants.

### Data state and time

Late data, source failures, and corrections have different meanings. A version identifier does not prove that an evaluation read one consistent state.

### Unusual data and alerts

Normal data varies by time and dataset. Weak baselines create false alerts, while one source failure can affect many datasets.

### Check completion and failures

Shared due times create bursts. External queries can outlive workers, and retries can repeat costly reads or publish results in the wrong order.

## Common mistakes

- The design assigns a separate full scan to every check.
- The design treats source errors, partial scans, or skipped checks as successful data checks.
- The design assumes that version identifiers guarantee consistent snapshot reads.
- The design claims a hard global scan limit from estimates, cancellation, or independent tenant budgets.
- The design ignores expected arrival schedules and reuses metrics after data corrections.
- The design starts with complex anomaly models before it defines coverage, baseline readiness, and incident behavior.

## The bar by level

- **Senior:** Estimates query cost and capacity. Reuses valid metrics, handles corrections, and ties results to verified snapshots. Defines baseline readiness and retries. Enforces source caps through global reservations.
- **Staff+:** Proves the scan limit during uncertain submissions, retries, day changes, and failover. Defines exact-result guarantees for each source capability. Resolves fairness and recovery costs.

Practice this question with an AI interviewer: https://sysround.com/practice/new?problem=data-quality-monitoring


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SysRound (https://sysround.com) is an AI practice tool for system design interviews. It has a voice or typed AI interviewer and a shared whiteboard. At the end, you get feedback on each skill and a model answer. The AI feedback can be wrong.