Rewiring My Career: How I Transitioned from Electrical Engineering to Data Engineering | by Loizos Loizou | Nov, 2024

It’s worth mentioning that this textual content comes from an Electrical and Digital Engineer graduate who went all one of the simplest ways and spent just about 8 years in academia learning in regards to the Vitality sector (and after I say all one of the simplest ways, I suggest from a bachelor diploma to a PhD and postdoc). Although that’s moreover a extreme demand occupation throughout the job market, I decided to change to an data engineer path instead.

I constantly come all through posts in boards and blogs the place people from completely completely different disciplines ask about simple strategies to swap to a occupation in data. So this article will take you through my journey, and the way in which an engineering graduate has nothing to worry in regards to the transition to this new self-discipline. I’ll bear {the marketplace} for data jobs, my story, and the skills that engineers have (whether or not or not it’s electrical, mechanical, digital and so forth.) that equip them successfully for this fast-paced self-discipline.

As experience continues to advance exponentially (IoT devices, AI, web firms and so forth.) so does the amount of data generated each single day. The result from this? The need for AI and Data professionals is presently at an all time extreme and I really feel it’s solely going to get higher. It’s presently at a level that the demand for these professionals severely outgrows present and new job listings are popping out each single day.

In response to Dice Tech Job Report, positions like Data Engineers and Data Scientists are amongst the quickest rising tech occupations. The reason being that corporations have lastly come to the idea that, with data, you’ll be capable to unlock limitless enterprise insights which could reveal their product’s strengths and weaknesses. That’s, if analyzed the fitting strategy.

So what does this suggest for the long run data professionals in quest of a job? The subsequent should be true, at least for the next few years:

1. Limitless job listings: In response to a present report by LinkedIn, job postings for AI roles have surged by 119% over the earlier two years. Equally, data engineering positions have seen a 98% enhance. This highlights the urgency of corporations to lease these sort of pros.
2. Extreme wage potential: When demand exceeds present, it immediately leads to higher salaries. These are fundamental authorized pointers of economics. Data professionals in the meanwhile are in an interval the place they’ve quite a few decisions for a job since corporations acknowledge the value they carry to their agency.
3. Various commerce alternate options: Take my case as an example. I labored in data for energy, retail and finance sectors. I ponder myself data agnostic, since I’m now prepared to pick out from alternate options all through a relatively extensive number of industries.
4. Future job improvement: As talked about sooner than, the need for these professionals is barely going to get higher since data is out there in all sizes and kinds and there’s a need for those who know simple strategies to take care of it.

So proper right here comes the million dollar question: How can an Engineer, whether or not or not that’s mechanical, digital, electrical, civil and so forth. swap to a occupation in data? Good question.

Is it simple? No. Is it worth it? Undoubtedly. There’s no proper reply for this question. Nonetheless, I can let you realize my experiences and chances are you’ll determine by your self. I can also let you realize the similarities I found between my engineering diploma and what I’m doing now. So let’s start.

A brief story of how I switched to data engineering:

Years 2020–2022

The yr is 2020 and I’m about to finish my PhD. Confused about my decisions and what I can do after a protracted 4-year PhD (and with a excessive imposter syndrome too), I chosen the safe path of academia and a postdoc place at a Evaluation and Enchancment center.

Whereas working there, I noticed that I need to get out of academia. I not had the vitality to be taught additional papers, proposals or, far more so, write journal and conference papers to showcase my work. I did all these — I had adequate. I had like 7–8 journal/conference papers that acquired revealed from my PhD and I didn’t truly like the reality that that’s the one method to showcase my work. So, I started in quest of a job throughout the commerce.

In 2021, I managed to get a job in energy consulting. And guess what? Additional tales, additional papers and even greater, PowerPoint slides! I felt like my engineering days had been behind me and that I’d truly do nothing useful. After a quick stint at that place, I started in quest of jobs as soon as extra. One factor with technical challenges and that implies that acquired my thoughts working. That’s after I started in quest of data professions the place I’d use the skills that I acquired all by way of my occupation. Moreover, this was the time that I acquired most likely probably the most rejections in my life!

Coming from very worthwhile bachelor and PhD ranges, I couldn’t understand why my experience weren’t fitted to a information place. I was making use of to data engineer, analyst and scientist positions nonetheless all I acquired was an computerized reply like “Sadly we are going to’t switch forward collectively along with your software program”

That’s after I started making use of to truly in all places. So when you’re learning this because of you’ll be capable to’t make the swap, think about me. I get you.

Years 2022–2023

So, I started making use of in all places to one thing that even pertains to data. Even to positions that I didn’t have any of the job description experience. That’s the place the magic occurred.

I acquired an interview from a corporation throughout the retail sector for the place of “Enterprise Intelligence Authorities”. Are you conscious what this place is about? No? That’s correct, I didn’t each. All I seen throughout the job description was that it required 3–5 years of experience in Data Science. So I assumed, this has one factor to do with data, so why not. I acquired the job and commenced working there. Appears that “Enterprise Intelligence” was a job description that was principally enterprise intelligence for the commercial division. Lucky me, it was spot on. It gave me the prospect to start out out experimenting with enterprise intelligence.

In that place, I used Vitality BI at first, given that operate was about setting up tales and dashboards. Then, I was hungry for additional. I was fortunate that my supervisor was very good so he/she trusted me to do regardless of I wanted with data. And so I did.

Sooner than I knew it, my engineering experience had been once more. All the problem fixing experience that I acquired all by way of the years, the bug for fixing challenges and the publicity to completely completely different programming languages started connecting with each other. I started setting up automations in Vitality BI, then extended this to writing SQL to automate additional points after which setting up data pipelines using Python. In 1 yr’s time, I had all my processes nearly automated and I knew that I had the technical performance to sort out harder and technically intensive points. I constructed unbelievable dashboards that launched useful insights to the enterprise householders and that felt unbelievable.

This was the lightbulb second. That this occupation, it doesn’t matter what the information is about, was what I was in quest of.

Years 2023-present

After one and a half years on the agency, I knew it was time to go for one factor additional technically troublesome than merely enterprise intelligence. That’s when an opportunity turned out for me for a information engineer place and I took it.

For the earlier one and a half years I’ve been working throughout the finance sector as a information engineer. I expanded my data to additional points paying homage to AI, real-time streaming data pipelines, APIs, automations and lots additional. Job alternate options are growing frequently and I actually really feel fortunate that I’ve made this swap, and I couldn’t advocate it adequate. Was it troublesome? I’ll say that the one troublesome half in every BI and knowledge engineering positions was the first 3 months until I acquired to know the devices we use and the environments. My engineering expertise outfitted me successfully to deal with completely completely different points with pleasure and do very good points. I wouldn’t change my diploma for the remaining. Not even for a Computer Science diploma. How did my engineering diploma help all by way of this transition? That’s talked about throughout the subsequent half.

How Engineering equips you with experience that help in a information occupation

So when you occur to’ve be taught this far, it’s important to be questioning: How is my engineering diploma preparing me for a occupation in data? This man has knowledgeable me nothing about this. You’re correct, let’s get into it.

Engineering ranges are very important, not as a result of self-discipline nonetheless one of the simplest ways that they development the brains of those they analysis it. That’s my personal opinion, nonetheless going by my engineering ranges they’ve uncovered me to so many points and have prepared me to unravel points in every single bit that I actually really feel reasonably extra assured now. Nevertheless let’s get to the specifics. These are some key engineering experience that I see similarities and I get to utilize at my data operate every single day:

1. Programming: As {{an electrical}} and electronics engineer, I acquired publicity to quite a few programming languages all by way of my ranges. I used assembly language, Java, VHDL, C and Matlab. Likewise, I really feel completely different engineering disciplines do the equivalent issue since programming is a way to hold out simulations in engineering. Although I haven’t used Python or SQL all through my ranges, it was a seamless transition to these two, after getting uncovered to so many points. I’d even say nice, since I used to hate coding all through my bachelor diploma, nonetheless now I adore it. It most likely was a matter of tight deadlines and stress from so many points on the same time.
2. Draw back Fixing: I get to unravel points each single day nonetheless as my first faculty lecturer acknowledged to us on the very first day on the faculty, “Google is your pal”. If in case you could have a knack for fixing points, and also you’ve received been uncovered to one of the simplest ways engineering initiatives are handed out at universities (the place they principally give you a one paragraph description for the mission and depend on a product by the tip of the week), think about me you’ll be capable to resolve data points. You’ve got been by adequate preparation.
3. Math and Statistics: Engineering faculty college students get by intense arithmetic paying homage to linear algebra, calculus, statistics and others which will make you understand machine learning in a simple transition. It’s a bit powerful to grasp at first because of it’s a model new territory nonetheless you’ll get the cling of it.
4. Black Subject Points: I don’t even know if it’s a formal definition nonetheless I ponder “Black Subject” points to be these which may be terribly powerful to unravel, we’ve been using them, they work, nonetheless not a lot of individuals actually know what’s happening throughout the background. In data, the “Black Subject Draw back” is AI. It’s scorching, it actually works and it’s very good nonetheless no person truly is conscious of what’s happening throughout the background. Equally, engineering disciplines have their very personal “Black Subject” points. Optimistic, AI is hard nonetheless have you ever ever tried understanding the ability neighborhood downside? That’s no stroll throughout the park.
5. Modelling and Simulations: Every engineer pupil has been doing modelling and simulations and that’s nothing completely completely different from ML fashions and knowledge fashions.
6. Data Processing and Analytics: As an engineer pupil in my bachelor and PhD ranges I did a lot of data processing, transformation and analytics from oscilloscope recordsdata, sensor recordsdata and good devices that had tens of tens of millions of rows of data. These are examples of data pipelines as we title them throughout the data commerce. I didn’t truly know on the time though that this was the title for it. After I acquired to do it in an organization setting, these experience had been transferrable and helped lots.
7. Automations: Engineers hate repeated procedures. If there’s a way to automate one factor, they’ll do it. That’s the mindset {{that a}} data engineer desires. I carried this mindset to my data engineer place and it helps reasonably lots since I spend a lot of time automating stuff in my each day.
8. Presenting and explaining to non-technical people: One fairly frequent issue I was doing in my PhD was explaining my mission to non-technical people so that they’ll understand what I’m doing. This happens reasonably lots in data. You set collectively a lot of analysis for enterprise people so you’ll have to have the flexibility to make clear it too.

The entire above help me every single day in my data engineer place. Can you see the transferrable experience now?

Whereas I don’t want to encourage the entire engineering disciplines to leap right into a information place, I nonetheless assume that all engineers are useful, I wanted to jot down down this textual content to encourage the those who want to do the swap. There’s lots rejection as of late nonetheless on the same time alternate options. All you need is the exact various after which magic will adjust to since it’s attainable so that you can to make use of your experience. The very important issue is to keep up trying.

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