Design a data quality monitoring pipeline
PremiumData pipelineEasy
- Data quality
- Data freshness
- Anomaly detection
Quick answer
This data pipeline interview question asks you to design a data quality monitoring pipeline. Aim for a design that moves data from sources to consumers reliably and on time, then explain ingestion, processing, storage and trade-offs. Interviewers want to see how you handle volume, late or bad data and failures along the way.
The problem
Design a data quality monitoring pipeline: after every load into a warehouse of 2,000 tables, it checks freshness, row volume, nulls, duplicates and schema changes, alerts the owners and stops bad data reaching dashboards. The hard parts are cheap checks on huge tables, few false alerts and blocking only when it matters.
Today data problems are found by finance, or by an executive looking at a wrong dashboard. The goal is for the owning team to know first.
What must a data quality monitoring pipeline do?
- Checks: Freshness, row count, null rate, unique keys, allowed values and schema changes, defined per table.
- Rules and baselines: Owners set fixed rules (the key is unique), and the system learns baselines for metrics that vary, such as daily row count.
How well must it do it?
- Alert speed: An alert fires within 15 minutes of a bad load finishing.
- Low noise: Fewer than 10 false alerts per day across all tables.
What scale should you design for?
Use these numbers in your estimates.
The scale numbers unlock with this questionUnlock
Key concepts
- Data quality
- Data freshness
- Anomaly detection
- Schema drift
- Data contracts
- Circuit breakers
How you’ll practise
- Requirements
- Data volume
- Ingestion
- Storage and processing
- Serving and freshness
- Wrap-up
Lala, personalized to you, gives a hint when you ask and reviews your design when you are ready.
What do interviewers look for?
In any data pipeline design round.
- Clarifies sources, consumers and freshness needs
- Chooses batch, streaming or both with reasons
- Handles late, duplicate and malformed records
- Makes each stage safe to retry
- Explains storage formats and partitioning
- Plans monitoring, backfills and data quality checks
Questions people ask about this interview question
- Which design type is this question?
- It is a data pipeline question. You design a data quality monitoring pipeline and explain your choices the way you would in an interview.
- Is this question asked at entry level?
- It is marked Easy, so it suits engineers who are new to data pipeline interviews. It is also a good warm-up before harder questions.
- What should I cover first?
- Start with the data sources, the consumers and how fresh the data must be. Agree on scope with the interviewer before you design anything.
- Can I practise it for free?
- It is a premium question. Sign up and unlock it once for 10 credits; it then stays unlocked. When you finish, Lala, your personalized AI mentor, can review your design for 30 credits.
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Updated · Written by the SevyDevy team