Interviews/Data & Analytics/dbt Developer

Hire dbt Developers
who actually ship dbt that survives audits and merges

With EasyEnv we build a fit test for the role. Use it to cut the noise at the top of the funnel, or to go deep on the candidates who matter.

You can even evaluate how this person works with AI. So you hire the engineer who can ship, not the one who looks good in a slide.

dbt Core · CloudSnowflake · BigQueryTests · macrosExposures · lineageLive or take-home

Request a Demo Create a Free Account

Real Linux VM

Not a sandbox

systemd, kernel, full toolchain

Browser-based

Zero install for candidates

One link and they are in

Auto-graded

Pass / fail checks per challenge

Plus the full session replay

A great dbt developer writes models that pay rent: tested, documented, and reused. The good ones treat macros as code, exposures as contracts, and dbt CI as a real gate. EasyEnv hands the candidate a real dbt project with a flaky model and a stakeholder who wants the right number by lunch.

Inside a live interview

What you see when they work.

Real dbt project, real warehouse, every command recorded.

billing-api

AMS

Billing-api

src

billing.py

auth.py

models.py

tests

test_billing.pyM

test_auth.py

pyproject.toml

README.md

Pybilling.py

from decimal import Decimal

def calculate_total(items: list[dict]) -> Decimal:

# Sum line totals using Decimal for currency precision

total = Decimal("0")

for item in items:

total += Decimal(str(item["price"])) * item["qty"]

return total

candidate@dbt-workspace:~

alice@dev:~/billing-api$

● mainPython 3.12

Claude · ready

Claude

You

Refactor calculate_total to use Decimal for currency math, and add a test.

Claude

Ask Claude...

Mike

Sam

How it works

Three steps. Zero infra to maintain.

Pick the fit test

Use a ready-made challenge or ask us to build one for your stack. Live, take-home, or both.

Send one link

The candidate clicks. A full Linux workspace boots in their browser. No installs, no VPN, no zoom-share.

Review at 2x

Replay the session, read the auto-graded checks, and decide. Most reviews take under five minutes.

The problem

Why hiring a dbt Developer is hard.

dbt fluency hides under "I built a dashboard once".

Macro fluency separates engineers from analysts.

Test design is a craft. The interview has to find it.

CI for dbt is overlooked. Few candidates have set it up.

The fit test

What we measure for a dbt Developer.

Every area runs inside a real production-like workspace. The candidate works in a browser, the session records itself.

Model layering

A dbt project with no layers. They introduce staging/marts cleanly.

Test design

A model with silent nulls. They add tests that name the rule.

Incremental strategy

A rebuild-every-night model that should be incremental. They pick a strategy with intent.

Macros and packages

A repeated CASE statement. They extract a macro with arguments.

CI and exposures

A project with no CI. They wire slim CI and expose models with intent.

AI collaboration

Allow AI for SQL writing and grade whether they ran dbt test after.

The stack we put in front of them.

Real tools, pre-installed, accessible from the workspace terminal.

dbt Coredbt CloudSQLSnowflakeBigQueryRedshiftDatabricksdbt-utilsdbt-expectationsElementaryLightdashGitHub ActionsSlim CIJinjasqlfluff

Sample challenges

What a dbt Developer interview looks like.

Pick from our library or ask us to build a challenge for your stack. Each one is a real workspace, not a snippet.

Challenge 01

The model with silent nulls

Scenario

A revenue model with nulls that nobody tests. Downstream dashboards bleed.

What you learn

Whether they pick the right not_null and relationships tests.

In the workspace

$ ls challenges/

workspace, runbook.md, README.md

candidate has:

· a real Linux box in the browser
· kubectl, docker, terraform, jq, yq
· cluster, repo and cloud creds pre-wired
· auto-checks running in the background

interviewer sees:

· every keystroke + every command
· terminal + screen recording
· auto-graded pass/fail per check

AI in the loop

Score the engineer, and how they work with AI.

AI writes SQL that compiles and double-counts. EasyEnv records prompts so you see whether the candidate ran dbt test after the LLM-suggested refactor.

Allow or block AI per question, not per interview

Full prompt and response history replayed in the review

Score "verifies AI output" as a separate signal

Ready to hire a dbt Developer who ships?

Set up your first interview in under an hour. We will help you build a fit test for this role on your stack.

Request a Demo Create a Free Account

Role FAQ

Hiring a dbt Developer, answered.

Yes. Snowflake, BigQuery and Redshift sandboxes can be wired.

Yes. A dbt Cloud project can be linked on enterprise plans.

No. The workspace runs in their browser. Terminal, editor, ports and dashboards are all served from the EasyEnv workspace, so candidate setup is zero.

Each challenge has automated checks (did the service come back, did the test pass, did the right log message appear) plus a recording your team can replay. Most reviews take under five minutes.

Yes. AI access is a per-interview setting. Block it for fundamentals, then allow it later in the loop to assess AI collaboration separately.

Interviews/Data & Analytics/dbt Developer

Hire dbt Developers
who actually ship dbt that survives audits and merges

With EasyEnv we build a fit test for the role. Use it to cut the noise at the top of the funnel, or to go deep on the candidates who matter.

You can even evaluate how this person works with AI. So you hire the engineer who can ship, not the one who looks good in a slide.

dbt Core · CloudSnowflake · BigQueryTests · macrosExposures · lineageLive or take-home

Request a Demo Create a Free Account

Real Linux VM

Not a sandbox

systemd, kernel, full toolchain

Browser-based

Zero install for candidates

One link and they are in

Auto-graded

Pass / fail checks per challenge

Plus the full session replay

Inside a live interview

What you see when they work.

Real dbt project, real warehouse, every command recorded.

billing-api

AMS

Billing-api

src

billing.py

auth.py

models.py

tests

test_billing.pyM

test_auth.py

pyproject.toml

README.md

Pybilling.py

from decimal import Decimal

def calculate_total(items: list[dict]) -> Decimal:

# Sum line totals using Decimal for currency precision

total = Decimal("0")

for item in items:

total += Decimal(str(item["price"])) * item["qty"]

return total

candidate@dbt-workspace:~

alice@dev:~/billing-api$

● mainPython 3.12

Claude · ready

Claude

You

Refactor calculate_total to use Decimal for currency math, and add a test.

Claude

Ask Claude...

Mike

Sam

How it works

Three steps. Zero infra to maintain.

Pick the fit test

Use a ready-made challenge or ask us to build one for your stack. Live, take-home, or both.

Send one link

The candidate clicks. A full Linux workspace boots in their browser. No installs, no VPN, no zoom-share.

Review at 2x

Replay the session, read the auto-graded checks, and decide. Most reviews take under five minutes.

The problem

Why hiring a dbt Developer is hard.

dbt fluency hides under "I built a dashboard once".

Macro fluency separates engineers from analysts.

Test design is a craft. The interview has to find it.

CI for dbt is overlooked. Few candidates have set it up.

The fit test

What we measure for a dbt Developer.

Every area runs inside a real production-like workspace. The candidate works in a browser, the session records itself.

Model layering

A dbt project with no layers. They introduce staging/marts cleanly.

Test design

A model with silent nulls. They add tests that name the rule.

Incremental strategy

A rebuild-every-night model that should be incremental. They pick a strategy with intent.

Macros and packages

A repeated CASE statement. They extract a macro with arguments.

CI and exposures

A project with no CI. They wire slim CI and expose models with intent.

AI collaboration

Allow AI for SQL writing and grade whether they ran dbt test after.

The stack we put in front of them.

Real tools, pre-installed, accessible from the workspace terminal.

dbt Coredbt CloudSQLSnowflakeBigQueryRedshiftDatabricksdbt-utilsdbt-expectationsElementaryLightdashGitHub ActionsSlim CIJinjasqlfluff

Sample challenges

What a dbt Developer interview looks like.

Pick from our library or ask us to build a challenge for your stack. Each one is a real workspace, not a snippet.

Challenge 01

The model with silent nulls

Scenario

A revenue model with nulls that nobody tests. Downstream dashboards bleed.

What you learn

Whether they pick the right not_null and relationships tests.

In the workspace

$ ls challenges/

workspace, runbook.md, README.md

candidate has:

· a real Linux box in the browser
· kubectl, docker, terraform, jq, yq
· cluster, repo and cloud creds pre-wired
· auto-checks running in the background

interviewer sees:

· every keystroke + every command
· terminal + screen recording
· auto-graded pass/fail per check

AI in the loop

Score the engineer, and how they work with AI.

AI writes SQL that compiles and double-counts. EasyEnv records prompts so you see whether the candidate ran dbt test after the LLM-suggested refactor.

Allow or block AI per question, not per interview

Full prompt and response history replayed in the review

Score "verifies AI output" as a separate signal

Ready to hire a dbt Developer who ships?

Set up your first interview in under an hour. We will help you build a fit test for this role on your stack.

Request a Demo Create a Free Account

Role FAQ

Hiring a dbt Developer, answered.

Yes. Snowflake, BigQuery and Redshift sandboxes can be wired.

Yes. A dbt Cloud project can be linked on enterprise plans.

No. The workspace runs in their browser. Terminal, editor, ports and dashboards are all served from the EasyEnv workspace, so candidate setup is zero.

Each challenge has automated checks (did the service come back, did the test pass, did the right log message appear) plus a recording your team can replay. Most reviews take under five minutes.

Yes. AI access is a per-interview setting. Block it for fundamentals, then allow it later in the loop to assess AI collaboration separately.

How to Hire a dbt Developer | Model · Test Interviews | EasyEnv | EasyEnv

Hire dbt Developerswho actually ship dbt that survives audits and merges

What you see when they work.

Three steps. Zero infra to maintain.

Pick the fit test

Send one link

Review at 2x

Why hiring a dbt Developer is hard.

What we measure for a dbt Developer.

Model layering

Test design

Incremental strategy

Macros and packages

CI and exposures

AI collaboration

The stack we put in front of them.

What a dbt Developer interview looks like.

The model with silent nulls

Score the engineer, and how they work with AI.

Ready to hire a dbt Developer who ships?

Hiring a dbt Developer, answered.

Hire dbt Developerswho actually ship dbt that survives audits and merges

What you see when they work.

Three steps. Zero infra to maintain.

Pick the fit test

Send one link

Review at 2x

Why hiring a dbt Developer is hard.

What we measure for a dbt Developer.

Model layering

Test design

Incremental strategy

Macros and packages

CI and exposures

AI collaboration

The stack we put in front of them.

What a dbt Developer interview looks like.

The model with silent nulls

Score the engineer, and how they work with AI.

Ready to hire a dbt Developer who ships?

Hiring a dbt Developer, answered.

Hire dbt Developers
who actually ship dbt that survives audits and merges

Hire dbt Developers
who actually ship dbt that survives audits and merges