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dbt & Analytics Engineering Interview Questions Interview Guide

10 interview questions with sample answers

12-15 hours
Prep Time
$140K-$220K
Salary
10
Questions

About This Role

Master dbt: data transformation, testing, documentation, and building reliable data pipelines with software engineering practices.

Behavioral Questions (2)

Q1

Tell me about a dbt project you led. How did you structure models?

Sample Answer:

Built dbt project with 200+ models. Organized: staging (raw), intermediate (business logic), marts (reporting). Implemented testing (95% coverage), documentation, CI/CD integration. Reduced query time by 40%.

Q2

How have you implemented data quality testing in dbt?

Sample Answer:

Added dbt tests: unique, not_null, relationships, custom tests. Implemented schema validation, referential integrity checks. Caught data issues 2 days earlier on average.

Technical & Situational Questions (4)

Q3

Explain dbt models: table vs view vs incremental. When would you use each?

Sample Answer:

Table: full rebuild, slower but complete. View: no storage, always current. Incremental: append new rows, fastest. Use incremental for large fact tables, views for lightweight dimensions.

Q4

How do you manage dbt project structure and dependencies?

Sample Answer:

Organize by layers: staging, intermediate, marts. Use refs for dependencies, enable dbt to build lineage. Tag models for execution patterns (daily, weekly). Use directories to separate concerns.

Q5

Explain dbt sources and seeds. How do you version manage data?

Sample Answer:

Sources: external tables, test and document them. Seeds: CSV files for static data, version in Git. Use for: dimension tables, lookup tables, test data.

Q6

How would you implement CI/CD for dbt?

Sample Answer:

Run dbt test in PR pipeline, fail if tests fail. Deploy to prod on merge. Use dbt Cloud or orchestration tool. Test in dev environment before prod deploy.

FAQ

When should I use dbt vs custom SQL scripts?
dbt for: team projects, testing, documentation, version control. Custom SQL for: one-off queries, extreme performance needs. dbt is better for maintainability.
How do I test dbt models effectively?
Combine dbt tests (unique, not_null) with custom tests (business logic). Test at staging and mart levels. Aim for 80%+ coverage.
Can dbt handle complex transformations?
Yes, use dbt macros for reusable logic, Jinja2 for templating. Consider custom dbt operations for very complex tasks.
How do I version dbt projects?
Use Git for code versioning. Tag production-ready commits. Use dbt Cloud environments for staging/prod separation.

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Last updated on 2026-03-07