Four years ago, I was sitting in a lecture hall at Pulchowk Campus learning about signal processing and RF communication circuits. I had just been accepted into one of Nepal's most competitive engineering programs. My parents were proud. My peers were jealous. Everything was on track.
By third year, I knew I'd made a mistake.
Not because ECE was hard. Not because I wasn't interested. But because I looked at the job market for electronics engineers in Nepal and realized: there weren't any. At least, not at a scale where I could build a real career without leaving the country.
This is the part of the career pivot story that nobody talks about. It's not some inspiring "I found my true passion" moment. It's more practical: I looked at what jobs existed, what they paid, and what growth was possible. Then I worked backward from there.
Let me walk you through how that actually happened.
The ECE Reality Check (Third Year, Mid-COVID)
By 2020, my batch was starting to think about internships and job prospects. Here's what we found:
Electronics Engineering jobs in Nepal:
- Nepal Telecom (hiring 5 people per year)
- A few small telecom equipment companies
- Hardware manufacturers (mostly assembly lines, not design)
- Embedded systems roles (extremely limited)
Average salary: 20,000 to 30,000 NPR per month (~$150-200 USD) for entry-level. Some people ended up taking any job just to have something.
What happens next: Most grads either:
- Go abroad (USA, UK, Middle East) for postgrad studies and jobs
- Stay in Nepal and take lower-paying jobs
- Pivot to software (which is what smart ones do)
The COVID lockdown happened. In-person lectures stopped. The energy of being at Pulchowk—one of the best engineering colleges in Nepal—disappeared. I was home in Kathmandu with all the time in the world to think about what I was actually doing.
So I started looking at job postings. Not ECE jobs. Just jobs. Anything in tech.
The Moment I Saw "Data Engineer"
LinkedIn and Nepali job boards started showing roles I'd never heard of: Data Engineer.
The postings looked like this:
"Data Engineer — Python, SQL, Apache Spark, AWS, GCP. Build and maintain data pipelines. 3+ years experience. Salary: $10K-14K USD."
I read that and thought: "Wait. What? $10K for someone in Nepal working for a US company?"
Then I looked at the skills. Python. I could learn that. SQL. I'd written SQL before. Apache Spark. Never heard of it. AWS. Never heard of it.
But here's what mattered: Every other entry showed "Data Engineer" as an actual job category. Not 5 openings per year. Hundreds of openings. Companies were literally paying people to build data pipelines.
I had no idea what a data pipeline was.
The Self-Taught Journey (2020-2021)
I couldn't take a formal course. Pulchowk had no data engineering program. So I did what everyone does now: Coursera.
Started with:
- Python for Data Science (Coursera)
- SQL for Data Analysis (Coursera)
- IT Fundamentals (edX)
- Cloud Computing Basics (various sources)
This took about 6 months, studying at night while still doing ECE coursework during the day.
The problem: I had no idea if this was the right path. I was learning abstract concepts in a vacuum. Python for data science means nothing when you don't have a data pipeline to actually build.
Around month 4 of learning, something clicked. I watched a YouTube video on Apache Airflow—a tool that schedules data pipelines. The guy in the video set up a DAG (Directed Acyclic Graph) that:
- Extracted data from an API
- Transformed it with Pandas
- Loaded it into a data warehouse
- Scheduled it to run every day at 2 AM
And then... it just ran. By itself. Every day. Without human intervention.
I can't explain why that felt different from anything I'd built in ECE coursework. But it did. Building circuits is cool. Watching a circuit work is cool. But watching a system you built automatically process data in the background, day after day, without you touching it—that felt like real engineering.
That week I built my first ETL pipeline.
Impostor Syndrome in Production (2022-2024)
I got my degree in May 2022. By August 2022, I was hired as a Data Engineer at Fusemachines Nepal.
Here's the thing nobody tells you about pivoting fields: Your first 2 years will feel like you don't belong.
I was surrounded by Computer Science graduates. They'd been coding since school. They knew data structures, algorithms, Big O notation. They talked about database indexing like I talked about circuits. I'd built 1 full pipeline before getting the job. They'd built 20.
The imposter syndrome was real.
I remember sitting in a code review meeting thinking: "Everyone here learned programming as a skill. I learned it because I needed a job. How long until they figure out I don't belong?"
I'd wake up some mornings and think: "I studied electronics. I have no business working with data systems."
Here's Why That Thinking Was Wrong
About 6 months into the job, I realized something: My ECE background wasn't a disadvantage. It was a superpower I didn't know I had.
Signal processing? That's literally analyzing data patterns. Circuits? Systems thinking—input, transformation, output. Communication theory? Understanding data flow and bottlenecks.
When my team hit a Spark performance problem, I thought about it like I'd think about signal degradation. Noise, loss, bandwidth. Spark is processing data (signal), transformers are operations (circuits), and worker nodes are processors (processing units).
The technical concepts mapped directly. I just had to translate the vocabulary.
What ECE actually gives you:
- Systems thinking: How pieces fit together
- Constraints and tradeoffs: Every decision has cost
- Debugging at multiple levels: Hardware diagnosis is similar to data pipeline diagnosis
- Precision mindset: Off by 0.1 dB is a problem. Off by one partition size is a problem.
I realized: The degree doesn't matter. The thinking patterns matter.
The Timeline (What Actually Happened)
Here's the actual progression:
2020-2021: Self-taught Python, SQL, cloud basics
Aug 2022: Hired at Fusemachines as Data Engineer (entry-level salary, no experience)
2023: Databricks Certified Data Engineer Associate
Early 2024: Microsoft Fabric Data Engineer Associate DP-700
Mid 2024: Fabric Analytics Engineer Associate DP-600
Late 2024: Azure Fundamentals AZ-900
2025: Astronomer Airflow Fundamentals (in progress)
Each certification was 2-3 weeks of study while working full-time.
Certifications aren't everything. But they're a signal—to employers, to yourself—that you're learning. Each one represented: "I spent 40 hours on this, I passed a test, I actually know this."
The Reality of Being a Data Engineer in Nepal
Working from Nepal for US-based companies is actually pretty common now. Here's why:
Timezone advantage: Nepal is UTC+5:45. US is UTC-5 to UTC-8. Perfect overlap for async work.
Cost efficiency: Salaries are reasonable for Nepal, very cheap for US companies.
Time zone: We're awake when US teams are sleeping. When a US team leaves at 5 PM, we start our shift and handle production issues or finish work.
Skills market: Data engineering skills are in massive demand. There's literally a shortage. Companies hire the best people regardless of location.
The reality:
- Working for US companies from Nepal is completely normal
- Salaries are 5-10x what you'd make working for Nepali companies
- You get exposed to best practices and scale (production Spark clusters processing TB of data)
- Imposter syndrome is real but becomes less real as you ship code and things work
What I Wish I'd Known (Lessons from the Pivot)
1. Your Degree Field Doesn't Determine Your Career
I studied electronics. I work with data. Some people I know studied EEE and became DevOps engineers. Others studied civil engineering and became cloud architects.
Your degree teaches you how to think. The specific technical skills are just the delivery mechanism.
Lesson: Don't trap yourself thinking "I studied X so I must do X." Learn from what excites you, not what's in your major curriculum.
2. Self-Learning at Scale is Real
I didn't go to a bootcamp. Didn't do a masters. Just Coursera, YouTube, and building real projects.
The credentials that matter:
- Certifications (shows you actually studied)
- GitHub projects (shows you can ship code)
- LinkedIn profile with real experience (shows you've worked on scale)
The degree matters less every year.
Lesson: If you want to pivot, pick skills, take courses, build a project, put it on GitHub, and apply. The degree will open the first door, but the skills open all the others.
3. Imposter Syndrome is a Feature, Not a Bug
If you feel like you don't belong, that's actually a sign you're in the right place—growing. The moment you feel like an expert, you've stopped learning.
My first year I felt like a fraud. By year 2, I was debugging complex Spark issues. By year 3, I was designing data systems.
Lesson: Imposter syndrome means you're outside your comfort zone. That's where growth happens. Don't fight it; use it.
4. The Data Engineering Job Market is Real
This isn't a bubble. Data engineering is becoming as core to business as software engineering was 20 years ago.
Every company collects data. Every company needs pipelines. Every pipeline needs someone to build it.
Lesson: If you're worried about job security, data engineering is about as safe as it gets right now.
A Message to ECE Students Reading This (Especially in Nepal)
I know some of you picked ECE because:
- Your parents said engineering is respectable
- You got in and felt obligated
- You thought you'd work in hardware/telecom
- You didn't have other options at the time
And I know some of you are realizing now that ECE doesn't match the job market.
Here's what I want you to know:
1. It's not too late to pivot. I pivoted with one year left. You can pivot at any point. During school, after graduation, 5 years into an ECE job. It doesn't matter.
2. Your degree isn't wasted. The math, the systems thinking, the precision mindset—all of it transfers. You're not starting from zero.
3. The data engineering job market is open. Right now, in 2025, there are more data engineering jobs than there are data engineers to fill them. If you learn SQL and Python, you can get a job. I know because I did.
4. You don't need a CS degree. That's the secret everyone keeps. CS degree is nice but not required. Consistent learning + a portfolio of real projects + certifications > a CS degree from the wrong college.
5. Start local, think global. Get a job at a Nepali company first (or remotely for a US company). Build experience. Get certified. Then you can work anywhere.
Where I Am Now
- Working for Fusemachines Nepal as a Data Engineer
- Building Spark pipelines for US-based clients
- Mentoring junior engineers who are also pivoting
- Writing about data engineering so others can learn from my path
- Applied for AWS re:Invent 2026 All Builders Welcome Grant (waiting to hear back)
- Still learning. Will always be learning.
The imposter syndrome is mostly gone. Not completely—that's healthy. But I've built enough production systems, debugged enough data issues, and shipped enough code to know: I belong here.
And so can you.
For ECE Students Considering This Path
Pick 3 skills. Master them in 6 months:
- Python — Not for web apps, for data. Learn pandas, numpy, matplotlib.
- SQL — Learn database design, complex queries, indexing.
- One data tool — Spark, Airflow, dbt, whatever interests you.
Build 1 real project:
- Extract data from an API
- Transform it with Python/SQL
- Load it into a database or data warehouse
- Schedule it to run automatically
- Write about it
Put it on GitHub. Write a blog post. Share it.
That's your portfolio. That's your proof you can do this job.
Then apply. When they interview you, be honest: "I studied ECE but I'm passionate about data." Show them your project. Show them your certifications. Tell them what you've learned.
They'll hire you. Because they need data engineers and you're one of the few people willing to learn.
What made you consider pivoting careers? What held you back? I'm curious about your story. Comment below.
And if you're an ECE student reading this at 2 AM while contemplating life choices, know this: You're not alone. Many of us have been there. And it gets better. Way better.
