Interviews/Infrastructure & Reliability/Observability Engineer

Hire Observability Engineers
who actually make the unknown visible

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.

Prometheus · GrafanaOpenTelemetryLoki · TempoDatadog · HoneycombLive 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

An observability engineer makes the difference between a 30-minute and a 30-second incident. The good ones think about cardinality, sampling and dashboard fatigue. EasyEnv puts the candidate in a workspace with a service that is under-instrumented and a vague complaint to chase.

Inside a live interview

What you see when they work.

Real Prometheus, real service, 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@observability-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 Observability Engineer is hard.

Instrumentation is invisible work until it pays off. CVs underplay it.

Cardinality matters. The wrong label turns a metric into a bill.

Dashboards become wallpaper. The interview has to test taste, not just tools.

Logs, metrics and traces complement each other. Few candidates use all three well.

The fit test

What we measure for a Observability Engineer.

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

Metric design

An endpoint with no metrics. They add the right ones with the right labels, and explain.

OTel instrumentation

A service in Go or Python without spans. They add OpenTelemetry without ballooning the trace count.

Cardinality reasoning

A label set that looked harmless and is now killing Prometheus. They fix it without losing signal.

Triage with what you have

A slow endpoint and three signals to choose from. They pick the cheapest path to a hypothesis.

Alert hygiene

A rules.yaml with noisy alerts. They cut the worst offenders without losing the real ones.

AI collaboration

Allow AI for query syntax and grade whether they verify the PromQL on real data.

The stack we put in front of them.

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

PrometheusGrafanaLokiTempoMimirOpenTelemetryDatadogHoneycombJaegerFluent BitVectorOTLPGoPythonJava

Sample challenges

What a Observability 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 blind service

Scenario

A Go service handling checkout has no traces and one ambiguous error rate metric. They instrument it without breaking it.

What you learn

Whether they choose span boundaries that match user actions.

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.

Observability is full of small PromQL or LogQL queries where AI is a fast typist. EasyEnv lets you watch whether the candidate runs the LLM-generated query and reads the cost before they commit it to a dashboard.

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 Observability 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 Observability Engineer, answered.

On enterprise plans we can connect a read-only mirror of your Prometheus, Loki or Tempo into the workspace.

Yes. The workspace runs a real OTel collector and a sample app emitting OTLP.

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/Infrastructure & Reliability/Observability Engineer

Hire Observability Engineers
who actually make the unknown visible

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.

Prometheus · GrafanaOpenTelemetryLoki · TempoDatadog · HoneycombLive 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 Prometheus, real service, 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@observability-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 Observability Engineer is hard.

Instrumentation is invisible work until it pays off. CVs underplay it.

Cardinality matters. The wrong label turns a metric into a bill.

Dashboards become wallpaper. The interview has to test taste, not just tools.

Logs, metrics and traces complement each other. Few candidates use all three well.

The fit test

What we measure for a Observability Engineer.

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

Metric design

An endpoint with no metrics. They add the right ones with the right labels, and explain.

OTel instrumentation

A service in Go or Python without spans. They add OpenTelemetry without ballooning the trace count.

Cardinality reasoning

A label set that looked harmless and is now killing Prometheus. They fix it without losing signal.

Triage with what you have

A slow endpoint and three signals to choose from. They pick the cheapest path to a hypothesis.

Alert hygiene

A rules.yaml with noisy alerts. They cut the worst offenders without losing the real ones.

AI collaboration

Allow AI for query syntax and grade whether they verify the PromQL on real data.

The stack we put in front of them.

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

PrometheusGrafanaLokiTempoMimirOpenTelemetryDatadogHoneycombJaegerFluent BitVectorOTLPGoPythonJava

Sample challenges

What a Observability 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 blind service

Scenario

A Go service handling checkout has no traces and one ambiguous error rate metric. They instrument it without breaking it.

What you learn

Whether they choose span boundaries that match user actions.

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 Observability 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 Observability Engineer, answered.

On enterprise plans we can connect a read-only mirror of your Prometheus, Loki or Tempo into the workspace.

Yes. The workspace runs a real OTel collector and a sample app emitting OTLP.

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 Observability Engineer | EasyEnv | EasyEnv

Hire Observability Engineerswho actually make the unknown visible

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 Observability Engineer is hard.

What we measure for a Observability Engineer.

Metric design

OTel instrumentation

Cardinality reasoning

Triage with what you have

Alert hygiene

AI collaboration

The stack we put in front of them.

What a Observability Engineer interview looks like.

The blind service

Score the engineer, and how they work with AI.

Ready to hire a Observability Engineer who ships?

Hiring a Observability Engineer, answered.

Hire Observability Engineerswho actually make the unknown visible

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 Observability Engineer is hard.

What we measure for a Observability Engineer.

Metric design

OTel instrumentation

Cardinality reasoning

Triage with what you have

Alert hygiene

AI collaboration

The stack we put in front of them.

What a Observability Engineer interview looks like.

The blind service

Score the engineer, and how they work with AI.

Ready to hire a Observability Engineer who ships?

Hiring a Observability Engineer, answered.

Hire Observability Engineers
who actually make the unknown visible

Hire Observability Engineers
who actually make the unknown visible