With clenched fists and an intensely furrowed brow, I stepped onto the sidewalk outside Facebook's Seattle office.
I found a seat on the edge of a planter and stared at the ground, involuntarily replaying the interview in my head over and over again.
I knew I had failed before I left the room. The question had thrown me. My mind went blank and my stomach turned to knots. Within minutes, I was panicking. Just like I had at Amazon a few weeks prior. Despite lifeline after lifeline patiently handed to me by the interviewer, I couldn't even find the starting line of a solution.
The call came a few days later. "We have decided not to move forward with your application," the recruiter said. She thanked me for my time and told me I could apply again in a year.
Another rejection. Surprise, surprise.
"Did I pick the wrong career?"
"It's because I did Computer Engineering and not Computer Science. Now I'm so far behind."
"I'm not cut out for this; my brain doesn't work that way."
"I'm just dumb."
These and many more punishing thoughts floated around in my head. Over the weeks and months that followed, the initial sadness gave way to a full-blown identity crisis.
Stuck in a Catch-22 (2014)
I had an interesting day job. My title was "Software Engineer," but I barely coded.
In my department at Boeing, anyone with 'engineer' in their title focused entirely on the airplane itself. We didn't maintain the supporting ground-based software systems. For that, we had programmer analysts. An engineer's job was to understand the customer's requirements, cross-reference them with their aircraft knowledge, and translate them for the analysts.
My day-to-day looked much more like that of a product manager than that of any contemporary software engineer.
This led to a textbook catch-22. I needed a job where I coded so I could get better at coding. But I couldn't get a coding job until I was better at coding.
I seriously considered giving up on coding altogether. I started looking for exits. Maybe I should pivot fully to Product Management? Maybe I should drop everything, take out a mountain of debt, and go back to school for an MBA? I even looked into switching paths entirely - law school, medical school, anything to escape the feeling that I had hit a dead end.
My journey out of this trap was long and winding. There was no single "aha!" moment. No inspiring movie montage. It took a decade of false starts, pivots, and stubborn resilience to finally close the gap.
The Up-Skilling Phase (2014–2017)
The first thing I did was to start coding at work. I mentioned previously that my job did not require me to code, but it was also not forbidden.
I wasn't going to start submitting PRs - I wouldn't have known how. But there was a database, and the engineers on the team often needed to pull data from that database into a CSV so they could perform some kind of analysis. That was a starting point. I learned some basic Python and SQL and started automating these lookups. Eventually, I became a go-to for these kinds of data pulls on the team.
Outside of the office, I hadn't given up on continued education. I grinded through a Java Development certificate. I took a "Data Structures and Algorithms" course at a local university to plug that gap from my undergrad. I worked through a Udacity Nanodegree in Frontend Development. Individually, none of these landed me where I wanted to be. But cumulatively, the skills and knowledge started to add up.
This entire time, I continued to apply and face rejection over and over again. Google, Apple, multiple startups and smaller companies in the area. Each time I would oscillate between doubling down and giving up on a career as a coder.
At the time, I didn't know what I was doing. I was just really stubborn and didn't want to give up.
Finally, in 2017, I just quit. I put in my notice at Boeing and enrolled in a Data Science bootcamp. The bootcamp involved long days of writing lengthy Pandas code. This daily practice was exactly what I needed. At the end of the bootcamp, I landed a job at a local company called Zonar where I was finally writing code every day.
The Leadership Trap (2017–2020)
The funny thing about my years as a "Software Engineer" who didn't code was that I was doing a lot of what tech companies consider "leadership." All that customer communication, project management, and presenting to executives actually ended up being super valuable in an organization filled with entry-level software engineers.
So, before long, the coding job morphed into a management role. Suddenly, I found myself leading a team of software engineers when I still didn't feel like I possessed deep technical competency myself. I was right back to where I started: not coding, and feeling like an imposter.
So I hit the books again. I signed up for Interview Kickstart - a brutal interview prep program heavy on LeetCode-style problems. Finally, I started to gain some semblance of confidence with coding interviews.
The Startup Forge (2020–2025)
This round of interviews led me to my startup phase, starting with a company called Gradient. Gradient retained its startup culture even after an acquisition by Criteo that happened while I was in the application process.
In a small team, the feedback loop is fast and brutal. Without the guardrails of more established companies, one bug can take out production or cost the company thousands in runaway compute spend. These lessons force growth. Fast.
From contacts at Gradient/Criteo, I found my way to Symbiosys, then a fledgling startup. The experience was similar, but even more intense. I was immediately trusted with a mountain of responsibility. This is where I honed the skills I had picked up by brute force earlier.
And then, the startup dream: Symbiosys was acquired by DoorDash.
Suddenly, I found myself at a top-tier tech company. But I could still hear the imposter syndrome rearing its ugly head: You didn't actually pass a FAANG-level interview loop to get here. You got brought in through the back door.
The Other Side of the Table (2025–2026)
Another turning point came when I started conducting technical interviews at DoorDash.
Over the course of six months or so, I conducted 10 to 20 interviews. I watched brilliant candidates freeze up. I watched mediocre candidates communicate flawlessly. I became adept at dropping hints and grading candidates, and I learned just how arbitrary the system could be.
The Message from the Past (2026)
I was comfortable at DoorDash. I liked the work, and I had finally conquered my technical self-doubt. I was good at what I did. Respected. Appreciated.
Then, a LinkedIn message. It was an old manager from Zonar - the company I had worked for years prior, right after leaving Boeing but before jumping into startups. A role had opened up on their team at Google, and they wanted me to apply.
Even with a manager referring you, nobody walks into Google without facing the gauntlet. The old anxiety tried to flare up, but this time, I knew what to do. I went back to the trenches. I grinded LeetCode.
I faced the ultimate tech loop. This time, I didn't freeze. This time, I knew exactly how to take the lifelines. When the loop ended, I walked away with an offer from Google.
What I Learned in the Trenches
Looking back at the decade it took me to get from that planter outside Facebook to an offer letter from Google, the biggest lessons weren't about syntax or algorithms. They were about the reality of building a tech career:
Whatever You Are Doing, Do It Well
If I had done the bare minimum at any previous job, I would not be where I am today. Regardless of the role I have been in, I have always been driven by a pride in ownership over my work. If I am going to do something, I want it done well. If I am going to build something, I want it to last. I think a lot of engineers share these values. In a world that's after quick fixes, it's easy to abandon these values just to "get it done." Don't.
Why? Caring about your work forces you to learn. If you do the bare minimum, it's likely you continue to use the same hammer for every nail, screw, or chicken nugget you see. The more times you stop and ask "what's the best way to do this," the more your experience will actually build on itself.
Another benefit to this: people notice. In a good way.
Growth Trajectories are Cumulative, Not Linear
Diving into Python and SQL at Boeing gave me a headstart at the Data Science bootcamp. Having that headstart helped me to land a great job at Zonar. That role set me up to learn infrastructure, data pipelines, REST APIs, and many more foundational skills. That exposure helped me tremendously when I landed at Gradient, tasked with building entire systems from scratch.
No single certificate, bootcamp, or course fixed my career. But cumulatively, they blended into a solid and unique foundation that would be impossible to replicate.
Yes, Data Structures and Algorithms Still Matter
I hear it all the time, and I used to say it myself: "Not once have I ever had to invert a binary tree in my real job!"
And that's true! But that was never why DSA interviews were used to screen candidates. Rather, DSA interviews are currently the best way to show a few specific things.
- You can communicate your thought process. It's impossible to recreate what it will be like to work with you on a technical problem. But DSA interviews are about as close an approximation as we have.
- You can reason through problems in a CS-specific way. Not just that you can reason through problems. But that when you see a hashing problem, you can recognize it as such. While manually implementing a hash function from scratch is rare, using the core concept of HashMaps to solve complex problems is common.
- You take this seriously. You were dedicated enough to spend countless hours grinding LeetCode or LeetCode-like problems.
And yes, this is still true in the age of AI. In fact, it's more important than ever. AI is already pretty good at blasting through hundreds of lines of mundane boilerplate code. With the easy but time-consuming stuff out of the way, our role as software engineers actually becomes more heavily concentrated on the most complicated technical questions. The questions that still require human judgment to get right.
At least for now.
Closing Thoughts
Everyone's path is different. No matter where they start. No matter where they are going. My hope with this piece is simply to provide an example of what a successful career path looks like. How it doesn't always look like success when you're in the middle of it. And how it can change and evolve over time.
For perspective, here's the full timeline in a condensed format:
2012 - Graduated from Embry-Riddle Aeronautical University and started working for Boeing.
2014 - Failed multiple high-profile tech screens (including Facebook and Amazon), sparking a sustained identity crisis.
2015–2016 - Earned a Java Development Certificate at CSUF and completed a Udacity Front-End Nanodegree while learning basic Python/SQL at work.
2017-Quit the stable aerospace track, completed a Data Science bootcamp, and landed a hands-on coding role at Zonar.
2017-2020 - Got pulled into leadership due to strong soft skills; completed Interview Kickstart to conquer technical imposter syndrome.
2020–2025-Dove headfirst into startup life at Gradient, rode through its acquisition by Criteo, and then carried experience with me to Symbiosys.
2025 - Symbiosys was acquired by DoorDash. Started conducting interviews, finally demystifying the hiring loop from the other side.
2026 - Faced the ultimate technical loop at Google without freezing. Received a formal software engineering offer.
This isn't a blueprint or an instruction manual by any means. Real careers don't always look neat and well thought out. Sometimes they are messy and convoluted. And that's just fine.
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