Interviews/Data & Analytics/Kafka Engineer

Hire Kafka Engineers
who actually ship Kafka systems that survive an outage

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.

Apache KafkaKafka StreamsConnect · Schema RegistryKRaftLive 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 Kafka engineer reasons about partitions, ordering and acks before they touch a topic. The good ones design idempotent producers, fix consumer lag without rewriting the world, and treat the schema registry as a contract. EasyEnv hands the candidate a real cluster with a rebalance storm and growing lag.

Inside a live interview

What you see when they work.

Real Kafka cluster, real consumer group, 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@kafka-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 Kafka Engineer is hard.

Kafka fluency hides under "I produced and consumed". Depth shows in partitioning.

Rebalance storms are the most common production fire.

Exactly-once is universally claimed and rarely shipped.

Schema evolution is where data goes wrong silently.

The fit test

What we measure for a Kafka Engineer.

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

Rebalance triage

A consumer group stuck rebalancing. They explain causes and fix.

Lag triage

A consumer 4 minutes behind. They scale or refactor with intent.

Exactly-once

A pipeline with at-least-once duplicates. They wire EOS or downstream dedup.

Schema evolution

A schema change breaking consumers. They explain compatibility before they ship.

Kafka Streams / Connect

A connector dropping records. They diagnose.

AI collaboration

Allow AI for code and grade whether they ran the consumer lag check after.

The stack we put in front of them.

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

Apache KafkaKRaftKafka StreamsKafka ConnectSchema Registry (Avro · Protobuf)MirrorMakerksqlDBStrimziConfluent Cloudcruise-controlJMXlibrdkafkaKarapaceFaust

Sample challenges

What a Kafka 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 group that will not settle

Scenario

A consumer group rebalancing every 30 seconds. JMX is open.

What you learn

Whether they reach for max.poll.records or session timeouts, not just restart.

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 consumer code that processes too much per poll. EasyEnv records prompts so you see whether the candidate watched rebalance counts after the LLM-suggested change.

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 Kafka 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 Kafka Engineer, answered.

Yes. Either control plane can be wired in the workspace.

Yes. A sandbox cluster can be connected 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/Kafka Engineer

Hire Kafka Engineers
who actually ship Kafka systems that survive an outage

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.

Apache KafkaKafka StreamsConnect · Schema RegistryKRaftLive 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 Kafka cluster, real consumer group, 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@kafka-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 Kafka Engineer is hard.

Kafka fluency hides under "I produced and consumed". Depth shows in partitioning.

Rebalance storms are the most common production fire.

Exactly-once is universally claimed and rarely shipped.

Schema evolution is where data goes wrong silently.

The fit test

What we measure for a Kafka Engineer.

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

Rebalance triage

A consumer group stuck rebalancing. They explain causes and fix.

Lag triage

A consumer 4 minutes behind. They scale or refactor with intent.

Exactly-once

A pipeline with at-least-once duplicates. They wire EOS or downstream dedup.

Schema evolution

A schema change breaking consumers. They explain compatibility before they ship.

Kafka Streams / Connect

A connector dropping records. They diagnose.

AI collaboration

Allow AI for code and grade whether they ran the consumer lag check after.

The stack we put in front of them.

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

Apache KafkaKRaftKafka StreamsKafka ConnectSchema Registry (Avro · Protobuf)MirrorMakerksqlDBStrimziConfluent Cloudcruise-controlJMXlibrdkafkaKarapaceFaust

Sample challenges

What a Kafka 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 group that will not settle

Scenario

A consumer group rebalancing every 30 seconds. JMX is open.

What you learn

Whether they reach for max.poll.records or session timeouts, not just restart.

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 consumer code that processes too much per poll. EasyEnv records prompts so you see whether the candidate watched rebalance counts after the LLM-suggested change.

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 Kafka 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 Kafka Engineer, answered.

Yes. Either control plane can be wired in the workspace.

Yes. A sandbox cluster can be connected 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 Kafka Engineer | Topic · Streams Interviews | EasyEnv | EasyEnv

Hire Kafka Engineerswho actually ship Kafka systems that survive an outage

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 Kafka Engineer is hard.

What we measure for a Kafka Engineer.

Rebalance triage

Lag triage

Exactly-once

Schema evolution

Kafka Streams / Connect

AI collaboration

The stack we put in front of them.

What a Kafka Engineer interview looks like.

The group that will not settle

Score the engineer, and how they work with AI.

Ready to hire a Kafka Engineer who ships?

Hiring a Kafka Engineer, answered.

Hire Kafka Engineerswho actually ship Kafka systems that survive an outage

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 Kafka Engineer is hard.

What we measure for a Kafka Engineer.

Rebalance triage

Lag triage

Exactly-once

Schema evolution

Kafka Streams / Connect

AI collaboration

The stack we put in front of them.

What a Kafka Engineer interview looks like.

The group that will not settle

Score the engineer, and how they work with AI.

Ready to hire a Kafka Engineer who ships?

Hiring a Kafka Engineer, answered.

Hire Kafka Engineers
who actually ship Kafka systems that survive an outage

Hire Kafka Engineers
who actually ship Kafka systems that survive an outage