Technical interview guide

Data Quality & Validation

Catching bad data before it reaches downstream consumers — schema checks, freshness, and anomaly detection.

Read: 29 min
Practice MCQs: 25
Interview QA: 25
Edition: v4
Editorial status: Reviewed
Relevant for: Data ArchitectData EngineerForward Deployed Engineer (FDE)

Scope: Great Expectations, dbt, Deequ, OpenLineage 1.53, JSON Schema 2020-12, and OpenAPI current 2026-08-31.

Interview QA

Treat each question like a live interview question: answer out loud first (structure, assumptions, tradeoffs), then open the model answer to spot gaps and rehearse a tighter follow-up.

Curated: 2026-10-05 · Written: 2026-09-11 · Reviewed: 2026-10-03

QA-1

Design a data-quality strategy for a business-critical analytical product.

QA-2

Define a producer data contract and validate it at ingestion.

QA-3

Measure and enforce data freshness and completeness correctly.

QA-4

Reconcile data across a source and transformed warehouse model.

QA-5

Design anomaly detection for data-quality metrics.

QA-6

Design pass, warn, quarantine, and block policies for quality failures.

QA-7

Build a quarantine and replay path for invalid data.

QA-8

Test and monitor the data-quality rules themselves.

QA-9

Design data-quality validation for an event stream.

QA-10

Validate data through a breaking or compatible schema evolution.

QA-11

Protect sensitive data in quality validation and profiling.

QA-12

Design quality controls for a multi-tenant dataset.

QA-13

Design data-quality checks for machine-learning features and labels.

QA-14

Choose between database constraints and pipeline data tests.

QA-15

Use sampling for large-scale data validation responsibly.

QA-16

Define data-quality observability and service objectives.

QA-17

Respond to a data-quality incident after bad data reached consumers.

QA-18

Validate a historical backfill or restatement.

QA-19

Control the cost of data-quality validation without losing critical assurance.

QA-20

Use lineage for quality impact analysis without overstating certainty.

QA-21

Migrate from ad hoc data checks to a governed quality platform.

QA-22

Recover data-quality validation after loss of results or metric history.

QA-23

Establish ownership and governance for data-quality controls.

QA-24

Capacity-test a data-quality validation system.

QA-25