Interviews/Data & AI/Data Engineer

Hire Data Engineers
who actually turn messy data into trusted tables

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.

Spark · DuckDBdbtAirflow · DagsterParquet · IcebergLive 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 data engineer trusts no source until they have checked it. The good ones think about lineage, idempotency and schema drift before they write a query. EasyEnv hands the candidate a pipeline that ran clean yesterday and is dropping rows today.

Inside a live interview

What you see when they work.

Real pipeline, real data, 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@data-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 Data Engineer is hard.

Data work is invisible until it is wrong. The interview has to show it.

Tool stacks vary wildly. Spark, dbt, Snowflake, BigQuery, Airflow. Few candidates know all of them.

Schema drift is silent. The candidate has to want to detect it.

Reruns and idempotency separate seniors from juniors. CVs cannot say which is which.

The fit test

What we measure for a Data Engineer.

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

Schema drift detection

A source where a column changed type. The candidate has the failed run and the lineage.

Pipeline design

A new pipeline from raw to analytics with reruns, partitions and tests.

Query performance

A 12-minute SQL on a real table. They explain the plan and cut it.

Data quality gates

A dbt project missing tests. They add the right ones.

Spark fluency

A PySpark job that OOMs. Real driver and executor logs are open.

AI collaboration

Allow AI for SQL and grade whether they ran EXPLAIN on what came back.

The stack we put in front of them.

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

SparkDuckDBdbtAirflowDagsterSnowflakeBigQueryRedshiftIcebergDelta LakeParquetKafkaFlinkPythonSQL

Sample challenges

What a Data Engineer 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 column that changed type

Scenario

An upstream source flipped a column from string to int. The pipeline ran green but the analytics tables are wrong.

What you learn

Whether they reach for lineage and contracts before they patch the SQL.

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 faster than it writes correct SQL. EasyEnv records every prompt, so you see whether the candidate ran EXPLAIN, sampled the data and validated the output against a known good before they merged the model.

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 Data Engineer 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 Data Engineer, answered.

On enterprise plans we can pre-wire a sandbox in either, with a copy of representative tables.

Yes. The workspace can run Kafka, Flink or Kinesis emulators for streaming challenges.

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/Data Engineer

Hire Data Engineers
who actually turn messy data into trusted tables

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.

Spark · DuckDBdbtAirflow · DagsterParquet · IcebergLive 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 data, 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@data-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 Data Engineer is hard.

Data work is invisible until it is wrong. The interview has to show it.

Tool stacks vary wildly. Spark, dbt, Snowflake, BigQuery, Airflow. Few candidates know all of them.

Schema drift is silent. The candidate has to want to detect it.

Reruns and idempotency separate seniors from juniors. CVs cannot say which is which.

The fit test

What we measure for a Data Engineer.

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

Schema drift detection

A source where a column changed type. The candidate has the failed run and the lineage.

Pipeline design

A new pipeline from raw to analytics with reruns, partitions and tests.

Query performance

A 12-minute SQL on a real table. They explain the plan and cut it.

Data quality gates

A dbt project missing tests. They add the right ones.

Spark fluency

A PySpark job that OOMs. Real driver and executor logs are open.

AI collaboration

Allow AI for SQL and grade whether they ran EXPLAIN on what came back.

The stack we put in front of them.

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

SparkDuckDBdbtAirflowDagsterSnowflakeBigQueryRedshiftIcebergDelta LakeParquetKafkaFlinkPythonSQL

Sample challenges

What a Data Engineer 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 column that changed type

Scenario

An upstream source flipped a column from string to int. The pipeline ran green but the analytics tables are wrong.

What you learn

Whether they reach for lineage and contracts before they patch the SQL.

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.

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 Data Engineer 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 Data Engineer, answered.

On enterprise plans we can pre-wire a sandbox in either, with a copy of representative tables.

Yes. The workspace can run Kafka, Flink or Kinesis emulators for streaming challenges.

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 Data Engineer | Real Pipelines | EasyEnv | EasyEnv

Hire Data Engineerswho actually turn messy data into trusted tables

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 Data Engineer is hard.

What we measure for a Data Engineer.

Schema drift detection

Pipeline design

Query performance

Data quality gates

Spark fluency

AI collaboration

The stack we put in front of them.

What a Data Engineer interview looks like.

The column that changed type

Score the engineer, and how they work with AI.

Ready to hire a Data Engineer who ships?

Hiring a Data Engineer, answered.

Hire Data Engineerswho actually turn messy data into trusted tables

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 Data Engineer is hard.

What we measure for a Data Engineer.

Schema drift detection

Pipeline design

Query performance

Data quality gates

Spark fluency

AI collaboration

The stack we put in front of them.

What a Data Engineer interview looks like.

The column that changed type

Score the engineer, and how they work with AI.

Ready to hire a Data Engineer who ships?

Hiring a Data Engineer, answered.

Hire Data Engineers
who actually turn messy data into trusted tables

Hire Data Engineers
who actually turn messy data into trusted tables