Interviews/Data & AI/ETL Developer

Hire ETL Developers
who actually move data without losing the truth

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.

Airflow · DagsterdbtSpark · DuckDBSnowflake · BigQueryLive 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 ETL developer worries about idempotency before they worry about throughput. They design backfills, not just pipelines. EasyEnv hands the candidate a real pipeline with a partial-run problem and asks them to make it safe to rerun.

Inside a live interview

What you see when they work.

Real pipeline, real warehouse, every keystroke 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@etl-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 ETL Developer is hard.

ETL bugs hide behind aggregates. The interview has to expose them.

Idempotency is taste, not configuration.

Tool stacks differ wildly. Few candidates know Airflow, Dagster and Step Functions.

Backfills are where careers are made or broken.

The fit test

What we measure for a ETL Developer.

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

Idempotent design

A pipeline that double-counts on rerun. They fix without rebuilding.

Backfill strategy

A request to backfill 2 years of data. They design without crushing the warehouse.

Schema drift

A source whose schema changed mid-day. The pipeline kept running.

Performance tuning

A slow stage. They explain before they crank resources.

Quality gates

A dbt suite missing the test that would have caught the bug.

AI collaboration

Allow AI for transformations and grade whether they sample the data before merging.

The stack we put in front of them.

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

AirflowDagsterPrefectdbtSparkFlinkDuckDBSnowflakeBigQueryRedshiftKafkaIcebergDelta LakePythonSQL

Sample challenges

What a ETL 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 total that grew on rerun

Scenario

A pipeline that triples a daily total when reran. The DAG, raw and curated tables are exposed.

What you learn

Whether they reach for upserts, MERGE or deduplication based on keys.

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 data SQL fast and forgets idempotency. EasyEnv records prompts so you see whether the candidate sampled both source and target before they merged the generated transformation.

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 ETL 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 ETL Developer, answered.

On enterprise plans, yes. Otherwise we use DuckDB or Postgres for realistic but cheaper tests.

Yes. Kafka and Flink emulators are available alongside the batch tools.

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 & AI/ETL Developer

Hire ETL Developers
who actually move data without losing the truth

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.

Airflow · DagsterdbtSpark · DuckDBSnowflake · BigQueryLive 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 pipeline, real warehouse, every keystroke 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@etl-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 ETL Developer is hard.

ETL bugs hide behind aggregates. The interview has to expose them.

Idempotency is taste, not configuration.

Tool stacks differ wildly. Few candidates know Airflow, Dagster and Step Functions.

Backfills are where careers are made or broken.

The fit test

What we measure for a ETL Developer.

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

Idempotent design

A pipeline that double-counts on rerun. They fix without rebuilding.

Backfill strategy

A request to backfill 2 years of data. They design without crushing the warehouse.

Schema drift

A source whose schema changed mid-day. The pipeline kept running.

Performance tuning

A slow stage. They explain before they crank resources.

Quality gates

A dbt suite missing the test that would have caught the bug.

AI collaboration

Allow AI for transformations and grade whether they sample the data before merging.

The stack we put in front of them.

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

AirflowDagsterPrefectdbtSparkFlinkDuckDBSnowflakeBigQueryRedshiftKafkaIcebergDelta LakePythonSQL

Sample challenges

What a ETL 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 total that grew on rerun

Scenario

A pipeline that triples a daily total when reran. The DAG, raw and curated tables are exposed.

What you learn

Whether they reach for upserts, MERGE or deduplication based on keys.

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 data SQL fast and forgets idempotency. EasyEnv records prompts so you see whether the candidate sampled both source and target before they merged the generated transformation.

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 ETL 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 ETL Developer, answered.

On enterprise plans, yes. Otherwise we use DuckDB or Postgres for realistic but cheaper tests.

Yes. Kafka and Flink emulators are available alongside the batch tools.

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 an ETL Developer | EasyEnv | EasyEnv

Hire ETL Developerswho actually move data without losing the truth

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 ETL Developer is hard.

What we measure for a ETL Developer.

Idempotent design

Backfill strategy

Schema drift

Performance tuning

Quality gates

AI collaboration

The stack we put in front of them.

What a ETL Developer interview looks like.

The total that grew on rerun

Score the engineer, and how they work with AI.

Ready to hire a ETL Developer who ships?

Hiring a ETL Developer, answered.

Hire ETL Developerswho actually move data without losing the truth

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 ETL Developer is hard.

What we measure for a ETL Developer.

Idempotent design

Backfill strategy

Schema drift

Performance tuning

Quality gates

AI collaboration

The stack we put in front of them.

What a ETL Developer interview looks like.

The total that grew on rerun

Score the engineer, and how they work with AI.

Ready to hire a ETL Developer who ships?

Hiring a ETL Developer, answered.

Hire ETL Developers
who actually move data without losing the truth

Hire ETL Developers
who actually move data without losing the truth