Inside a Tech Career: A Software Engineer Answers 7 Honest Questions
Half the day is meetings. The other half is writing Python, SQL, or JavaScript with AI. A tech professional shares what the industry actually looks like — and what they wish students knew.
Half the day is meetings. The other half is writing Python, SQL, or JavaScript with AI. A tech professional shares what the industry actually looks like — and what they wish students knew.
I'm Sean Ipakchi, a software engineer specializing in AI/BI analytics solutions. I work with tools like Claude Code, Sigma, Hex.tech, and Snowflake to build data products for clients. Ethan at Mentino asked me seven questions about what a career in tech actually looks like — here's the honest version.
The key to a successful career is to stay curious and cultivate a passion for learning. That's the main attribute that's allowed me to adapt and stay relevant in the fast-paced world of technology.
Being humble, spending time on YouTube, reading blogs and company docs, listening to podcasts, attending trainings, and going for certifications has all paid off in the long run. The best investment is in yourself.
Tech moves fast enough that skills that made you hireable three years ago may already be table stakes or obsolete. The people who survive multiple technology cycles — from mobile, to cloud, to AI — aren't necessarily the smartest. They're the ones who never stopped learning. Curiosity is the engine; humility is what keeps it running.
Morning agile daily syncs with my engineering team to share key information and statuses. Analyzing the next priority features we're building and what's left in the backlog. Any blockers or decisions we should address.
Then plenty of development time along with specific collaboration meetings or business development meetings to pursue other client project work. Could be a demo presentation that day as well.
I'd say half the day is meetings, the other half is writing Python, SQL, or JavaScript with AI.
This isn't ChatGPT writing everything for you. It's using AI tools like Claude Code or GitHub Copilot as a pair programmer — you still need to understand the code, debug it, and make architectural decisions. AI speeds up the writing; the judgment of what to write is still entirely yours. That's why communication and critical thinking matter more than ever, not less.
Sometimes we really get the architecture direction on the project wrong and there's either leftover tech debt, or we have to acknowledge that in front of the client and redirect our solution to a better architecture — and explain why and how long that will take.
There's no hiding from a bad architectural call. At some point you have to sit in front of a client, own it, and explain the path forward. That's genuinely hard.
Tech debt is what happens when you make fast decisions early that slow you down later. Every shortcut you take to ship quickly has interest — and at some point, the bill comes due. In consulting especially, you're accountable to a client who may not know the terminology but absolutely feels the slowdown. Learning to recognize architectural mistakes early — and communicating them honestly — is a skill that separates senior engineers from junior ones.
Pick a niche within technology you want to focus your time on and then spend a lot of time learning it to become an expert in that technology.
For me currently that's AI/BI Analytics solutions for clients involving Claude Code, Sigma, Hex.tech, and Snowflake. Go deep, not wide.
The mistake most students make is trying to learn everything — a little JavaScript, a little Python, a little data science, a little cloud. You end up mediocre at all of it. Pick something that has real job demand, sits at an intersection you find interesting, and go deep enough that you can actually build things. The niche Sean works in — AI-powered business analytics — is a good example: it combines data engineering, business understanding, and AI tooling in a way that has serious market demand right now.
That it's all tech bros. That there are always layoffs and something is wrong with the culture. That everyone is making a lot of money off stock and their salaries.
That you have to be an engineer to succeed in tech.
Product managers, UX designers, technical writers, data analysts, sales engineers, solutions architects, program managers, security specialists, DevRel — tech is a massive ecosystem, and most of these roles don't require you to write production code every day. If you're drawn to technology but don't want to be a software engineer, there are more paths than most people realize. The common thread is comfort with technical concepts and change, not necessarily deep coding expertise.
Reading comprehension, writing, and communication skills.
To be successful with other people and AI, those are required skills.
Counterintuitively, the rise of AI has made human communication skills more important, not less. AI tools respond to how clearly you can articulate what you want. Prompting is writing. Explaining a technical problem to a client is writing. Documenting a codebase so your team doesn't lose a week reverse-engineering your work is writing. The engineers and analysts who compound fastest are the ones who can translate between technical and human — and that's a writing and communication problem at its core.
I would have largely done the same as I did. Get into honors/AP classes, play a ton of video games, bike and run, meet a great group of best friends I'm still hanging out with today. Maybe being nicer to my parents — it's tough, I know.
Reach out to others more often to pick their brain, learn about what they're working on, and ask for help. It's taken a long time for me to swallow my pride and independence and ask others for help or fully collaborate on solutions. The students who accelerate fastest aren't the most talented — they're the ones who ask the best questions and are willing to let other people help them get there. That's a skill you can start practicing in high school.
Tech is bigger and more diverse than the stereotypes suggest. You don't have to be a certain type of person, come from a certain background, or know how to code on day one. What you do need is a genuine interest in learning, the willingness to go deep on something, and the communication skills to work with the humans — and the AI tools — around you.
If you're curious about what a career in tech, analytics, or AI actually looks like day to day, I'm on Mentino for exactly these kinds of conversations.
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