When I walked into the lab for my first day as a test engineer, I was buzzing with excitement. I had just transitioned from teaching physics, where failure was a teaching tool, not a financial liability. In the classroom, if something broke, you fixed it, learned from it, and moved on. But in a production test environment, I quickly learned that the stakes were much higher. A single missed connection, a power glitch, or a misconfigured setting could erase weeks of work, derail validation schedules, and cost thousands of dollars.

That reality hit me hard one morning when I discovered that a 21-day test had failed overnight. The power had gone out. There was no back up power supply or secondary server for data recovery. I stood there staring at the test stand, realizing I had done everything right except prepare for the one thing I hadn’t considered: infrastructure failure. That moment was a turning point.

I came to understand that great test engineers aren’t just data collectors. They’re risk managers, system designers, and resilience builders. They anticipate failure—not just react to it. Over the years, I’ve learned that confidence doesn’t come from getting everything right the first time. It comes from understanding why something failed and making sure it doesn’t happen again.

Here are seven hard-earned lessons I wish I had known when I started out. 

In my early days, I wasted countless hours tweaking sampling rates, filters, and loop timings before I truly understood the signals I was measuring. The result was inconsistent data, confusing results, and a long list of “possible causes.”

I learned that you can’t optimize what you don’t understand. Every new setup should begin at your desk, not on the test floor. Wire it up. Verify the ranges. Confirm the scaling. Make sure your software drives the hardware exactly as it will in the lab. That early validation catches most of the issues that would otherwise surface during a critical run.

Characterization is the foundation of confidence. Once you understand baseline behavior, how sensors respond, where noise enters, how timing behaves, optimization becomes intentional rather than reactive.

If you find yourself doing the same calculation more than twice, automate them.

One of my first major projects involved running hysteresis tests across a range of temperatures and pressures. It took three people and three days to acquire, analyze, and report the data. I rebuilt the entire process in NI LabVIEW so that a single click could run the sequence, analyze the results, and export a formatted report. The run time of the test didn’t change, but the total effort dropped from days to hours.

Automation isn’t just about speed. It’s about reliability and mental bandwidth. When repetitive tasks are automated, I can focus on the creative parts of engineering: interpreting data, improving designs, and planning the next iteration. Automating the predictable helped me better manage the unpredictable.

Data is only as trustworthy as the environment that produces it. That 21-day test failure taught me to treat infrastructure as seriously as instrumentation. From that point on, I made sure every critical bench had a UPS, monthly charge checks were routine, and documented power paths were non-negotiable.

Drift is another silent killer. Over time, sensors age, regulators drift, and environmental conditions creep out of range. I began to run periodic “golden” checks—quick sanity tests using known references—to catch slow failures before they ruin entire data sets.

A stable test system is like a stable foundation. When it works, no one notices. When it fails, no one forgets.

Early in my career, our data lived in chaos. Folders were labeled “final_v3,” “use_this_one,” and “newfinal_real.csv.” The issue arose when, months later, if a customer asked for verification, I would struggle to find the right file, or worse, I couldn’t identify which configuration it represented.

That experience taught me that data without metadata is just noise. After that, every file I generated included key tags: DUT ID, test protocol, firmware version, operator, environmental conditions, and configuration details. This small investment helped prevent massive confusion down the road.

Simply put, if two files could be swapped and no one would notice, that meant my metadata was too thin. Organized, searchable data isn’t just convenient. It’s the backbone of engineering integrity.  

Five minutes of inspection can save five hours of troubleshooting. Before connecting a new setup, always confirm every excitation, termination, and channel mapping. Make sure shield plans are correct, signal ranges are realistic, and grounding follows a consistent pattern.

Treat wiring like code. Review it, document it, and version it. A photo of each terminal block after tightening is worth more than memory when something starts acting strange a month later. Most “mystery signals” are just human error, a loose wire, a swapped channel, or an unplanned shared ground. A disciplined preflight habit keeps my test benches from turning into ghost stories.  

A good test report tells the full story: scope, setup, instrumentation, software version, data location, and analysis method. A report should include enough detail for another engineer to reproduce the results independently. That’s what builds trust, internally and with customers.

I once had to defend a set of results to a client after a design change. Because our report included the full updated test method, calibration certificates, and code version, the conversation took five minutes instead of five days. Reports that survive time protect both your reputation and your schedule.

Early in my career, I made the mistake of flooding managers with charts and data points. What they really wanted was one answer: “Are we ready to move forward?”

Framing updates in terms of risk reduction, confidence intervals, and next steps of what allows your stakeholders to understand the state of the test. That shift, from reporting activity to communicating impact, builds trust and earns engineers a seat at the decision table.

Communication isn’t a soft skill in engineering. It’s a force multiplier.  

Test engineering has taught me patience, humility, and precision. Looking back on those early days (panicking over power losses, chasing noisy signals, rewriting reports), I realize the biggest transformation wasn’t technical. It was my mindset.

The best engineers I’ve met treat testing as craftsmanship. They anticipate failure instead of fearing it. They automate the boring parts, document their logic, and create data that can outlive them. Their systems are resilient, their reports reproducible, and their confidence earned through preparation—not luck.

If you’re early in your test career, start by mastering the unglamorous parts: documentation, wiring, metadata, and consistency. They may not impress anyone right away, but they form the bedrock of trust. And in engineering, trust in the data is everything.

If you only do three things this week, do these:

1. Automate one repetitive task. 
2. Standardize your metadata. 
3. Run a dry-run bench test before going live. 

Every great test engineer learns these lessons, some before the 21-day test fails, others the hard way.