Be an Aware Engineer, Not Just an Efficient One
Most teams claim they want “high-performing engineers.”
Look closer and you’ll often see something else:
Dashboards measuring tickets closed
Pressure to “move fast” and “unblock delivery”
Quiet punishment for anyone who slows things down with awkward questions
In that environment, it’s easy to confuse being busy with doing good engineering.
This is not an attack on efficiency. It’s a warning:
If you only optimize for speed and throughput, you quietly train yourself to ignore learning, risk, and context.
That’s how you end up with fast engineers shipping dumb decisions.
When I say “engineer” in this piece, I mean everyone who designs, builds, tests, or operates the system. My own lens is test engineering, so the examples will lean that way.
What I care about isn’t just throughput. It’s something less visible in Jira and far more important in reality: awareness.
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Efficiency Is Simple. Awareness Is Work.
Efficiency is the easy part:
Automate repetition
Reuse patterns
Cut out obvious waste
Tools, frameworks, and CI pipelines make that easier every year.
Awareness is harder:
Seeing beyond “the ticket in front of me”
Understanding how a change interacts with the rest of the system
Recognizing who gets hurt if you’re wrong
Knowing when “quick fix” is acceptable and when it’s reckless
And unlike efficiency, you can’t just install awareness. You have to train it.
What Do I Mean by “Awareness”?
“Be more aware” is useless advice unless we break it down.
Awareness in engineering isn’t a feeling. It’s a set of models you carry in your head and update as you learn.
Here’s how I think about it. It’s at least five things:
System awareness
How data and control actually flow through the system.Which services touch this data?
What events trigger this behavior?
What assumptions are baked into the architecture?
Stakeholder awareness
Who is affected by this change and how.Which users depend on this?
What will support, ops, or sales feel if this goes wrong?
Who will be on-call when it explodes at 2 AM?
Risk awareness
What can go wrong, how likely it is, and how bad it would be.Is this a “UI glitch” risk or a “data corruption” risk?
If this fails silently, how long until we notice?
What’s the worst credible outcome?
Historical awareness
What has already failed around here.Has this area caused incidents before?
What did we learn last time and did we forget it?
Are we repeating an old mistake with a new name?
Constraint awareness
The real limits you’re under.How much time do we actually have?
What skills/tools are available right now?
What does this organization really reward: learning or speed?
When I say “be an aware engineer,” I’m not saying “think more.”
I’m saying: actively model these five things instead of pretending they don’t exist.
The System Often Rewards Blind Efficiency
It’s tempting to frame this as:
“Some engineers are blind and ticket-obsessed. They should be more aware.”
Reality is uglier.
Many teams and managers unintentionally punish awareness:
The engineer who asks, “What problem are we actually solving?” is labelled “blocking”
The engineer in a test role who says, “We need time to explore this area” is seen as “slowing things down”
The team that surfaces systemic issues is told, “Let’s just get this out and fix it later”
On the other hand, the system often rewards blind efficiency:
Close tickets quickly → praised
Reduce visible cycle time → praised
Ship with unknown risks that don’t explode immediately → forgotten
If you ignore this, the whole message collapses into moral advice:
“Engineers should be smarter and more aware.”
People respond to incentives. If awareness makes your life harder on your team, you will unconsciously avoid it.
That’s why awareness is not just a personal virtue. It’s also an act of resistance against metrics that only see speed.
A Bug Story: Narrow Model vs Wider Model
Let me ground this with a real story and treat it as a testing story, not a hero arc.
We had a bug that kept resurfacing in production.
Same area
Similar symptoms
Same level of irritation for users: not catastrophic, but not harmless
Round 1: The Narrow Fix
My initial mindset:
“We’ve seen this before.”
“I know roughly where it lives.”
“Let me just guard against that condition.”
I:
Found the failing condition
Added a defensive check
Updated a couple of tests around that path
Verified the reported reproduction and the happy path
Closed the ticket
Fast. Locally reasonable. Context: the team was under pressure, and this didn’t look like a “bring down the system” bug.
What I didn’t do:
Model where else this data was used
Ask, “Is this really the same bug as last time?” or just similar symptoms
Look at logs around the time of failure for other oddities
Talk to support about user impact beyond the written repro steps
My system awareness and risk awareness were narrow. Not zero, just shallow.
Two weeks later, the issue reappeared. This time in a different flow that depended on the same underlying behavior. Same family of problems, different surface.
Was my fix “wrong”?
Given the constraints and information I’d considered, it was locally fine.
But my model of the system was too small.
Round 2: Expanding the Model
When it came back, I approached it in a different mode: explicitly as a test engineer, not as someone trying to make a ticket disappear.
I wrote down a few questions:
“What do all these occurrences have in common?”
“In what situations does this not happen?”
“What would have to be true for this behavior to be impossible?”
Then I:
Traced the data through multiple services, not just the failing one
Looked at logs before and after the error, not only at the error line
Paired with another engineer and compared our mental models of the flow
Asked support which users reported it and what their environments had in common
Actively tried to break the system around the suspicious area with exploratory tests, not just confirm the known scenario
I stopped treating it as “the same bug is back” and started treating it as “a cluster of related failures sitting on top of misunderstood assumptions.”
We didn’t find a single magical “root cause.” We found a chain of conditions:
An assumption in validation logic
A subtle data contract expectation between two services
An error handling branch that swallowed important context
We changed several things:
The validation rules
How we surfaced certain errors
Some tests to reflect more realistic conditions, not just the neat happy-path input
That specific cluster of symptoms hasn’t resurfaced so far. More importantly, my approach to recurring issues changed.
This wasn’t “efficient engineer vs aware tester” as two different people.
It was the same engineer switching between shallow and deeper models under different pressure and incentives.
How Testing Sharpens Awareness
Testing is not something that happens after the “real engineering” is done.
Testing is engineering: it’s modelling, experimenting, and evaluating under uncertainty.
When you spend a lot of time in a testing role, you start to notice patterns:
The smell of a “just enough to make the test pass” fix
The way certain changes align with or fight the existing design
The blind spots in our oracles: things we never check because we didn’t imagine they could fail
Here are some testing-flavoured heuristics that connect directly to awareness:
When something keeps coming back
Treat it as a cluster, not “the same bug.”
Ask:
Where, exactly, have we seen this?
What was different each time: data, timing, user, environment?
What common decision or assumption could be behind all these failures?
When a fix feels too neat
Check your oracles.
Are we only verifying the behavior mentioned in the ticket?
What other side effects could this change have that we’re not checking?
If this area failed silently, what signal would we miss?
When pressure is high (“just patch it”)
Consciously pick your depth for this decision.
Ask:
If we go with the quick guard, what risks are we explicitly accepting?
Do we have a real plan to come back and investigate later, or is that a lie?
Who needs to know that we’re taking this shortcut?
That’s real context-driven thinking:
not “always go deep” or “always be fast,” but choosing how deep to go given risk, constraints, and stakes.
Engineers in Test as Awareness Multipliers
People working in a test-heavy role aren’t “gatekeepers after the fact.”
They are engineers whose primary tool is questioning and experimenting.
Good test engineers act as awareness multipliers:
They ask uncomfortable “what if” questions
They model user behavior the system was never designed for
They reveal interactions nobody considered when the feature was specced
Concrete ways this multiplies awareness for the whole team:
System awareness
Exploring integrations and edges, not just unit-level behavior.Stakeholder awareness
Thinking in real user flows, not just demo scripts.Risk awareness
Choosing what to explore based on impact, not just ease or habit.
A simple example:
An engineer changes a discount-calculation function and “all tests pass.”
The engineer in a test role comes in and asks:
“What happens when this overlaps with a promo code?”
“What if the user’s currency changes mid-session?”
“What does support see when this misbehaves?”
Suddenly the system looks different.
Not because the original engineer was stupid, but because someone brought in a different awareness lens.
Same profession. Different stance.
Tradeoffs, Not Slogans
I’m not going to claim “awareness always wins.” It doesn’t.
Sometimes:
The organization does not value awareness at all
The pressure is high enough that going deeper will cost you more than it’s worth
The risk is genuinely small and a quick guard really is the right call
Awareness doesn’t mean “always go deep.”
It means: know what you’re trading away when you stay shallow.
If there’s one habit worth stealing from this:
Whenever you’re about to ship a fix, ask:
“What am I assuming, and what happens if I’m wrong?”
That single question has saved me more pain than any automation framework.
Efficiency is useful. Tickets need to move.
But if you train yourself only to move fast, you’ll never see what you’re running past.
Awareness isn’t in your tooling.
It’s in your models, your questions, and your willingness to look beyond the ticket in front of you.
That’s the part that makes you more than just an efficient engineer. It makes you a dangerous one in the right way.
Further Reading
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