Alumni Spotlight


How Aaron Became a Data Engineer with Jigsaw Labs

By Jess Feldman
Last Updated April 27, 2022

Aaron always had an affinity for data and was fascinated by the process of data engineering. When he graduated from college without any applicable skills, Aaron looked to Jigsaw Labs to fill the gaps in his education and propel him into a data engineering career. Six months after the bootcamp and thanks to the externship experience offered at Jigsaw Labs, Aaron is now a full-time Data Engineer at Cred Protocol working in crypto! 

What inspired you to launch a career in data engineering?

I studied econometrics in undergrad, then I worked in IT at a TV station for a year, where I learned computer networking skills and got familiar with tech. Most recently, I worked as a translator in a medical setting. Growing up, everyone had told me to pick a major in college and then get a job as if there were an easy path laid out to connect those two goals. Unfortunately, I didn't emerge from college with any real skills. The structure of the school system failed me and I graduated feeling like I didn't really learn anything beyond y=mx+b. Realizing I had no real stepping stones to leverage for a stable career, I started envisioning my future and what I saw myself doing every day.

I’ve always been drawn to data engineering and had an affinity for data science, statistics, and probability. When I realized I wanted to pursue data engineering, I committed the next year to learning everything I could about it. I read other people’s code, online threads, and forums any chance I could. When it was time for my next step, my friend suggested checking out a bootcamp, since they’d just completed a software engineering bootcamp. 

Why did you choose data engineering over data science?

I chose data engineering over data science for two reasons:

  1. Data science as a field is saturated with job applicants. It also seems to require a master's or a doctorate degree to get a high-paying data science job. Knowing that traditional school had already failed me, graduate school didn’t seem like the right route. I did well in school but I wondered how I could quickly move toward a data career without going back to a university setting. 
  2. Data engineering is in high demand. Plus, I really like what’s involved in data engineering: the manipulation, the pipelines, the cleaning and formatting — that all really appeals to me. 

When researching data engineering bootcamps, what sets Jigsaw Labs apart?

As I was researching bootcamps, I came across Jigsaw Labs. After reading so many rave reviews about Jigsaw Lab’s Founder and Instructor Jeff Katz and the curriculum he built, the answer was obvious. 

Was there an application challenge?

There is a small programming assessment. Mostly, it tests your familiarity with Python fundamentals. Jigsaw Labs also gives you material to prep for the interview beforehand so that you are prepared for it. 

In your experience, did you feel like you had to know basic coding in order to apply to Jigsaw Labs?

It definitely helps to have some familiarity with programming, Python especially, even if you just read a little bit about it. I saw that it was a little easier for me having some previous experience, but there were people in my cohort who had never programmed before and they did fine in the bootcamp. It's definitely possible to attend Jigsaw Labs without having coded before because they are so patient and helpful to get you there.  

When you enrolled at Jigsaw Labs, what was your career goal? 

I planned on becoming a data engineer after graduating from the bootcamp and I’m now a Data Engineer at Cred Protocol. 

How did you balance the data engineering bootcamp with the rest of your life?

The bootcamp is really well-designed to accommodate all kinds of life situations, so you can maintain a full-time job if you need to. Classes are at night – three days a week and on weekends. Sundays are all-day marathons, which are a lot of fun! 

I foresaw that working and attending this bootcamp would require a lot of energy. I saved money for half a year, so that when I was in the bootcamp I could give it all my attention. That really paid off because I was able to put in more hours learning and practicing, but the program is designed so it's possible to juggle both work and bootcamp. When I wasn’t learning in class, I would use my extra time to take apart our labs and create my own versions. I learned a lot by tinkering with what I was learning. 

What did you actually learn in the data engineering bootcamp?

There are two main camps in data: Python and SQL. Jigsaw Labs pushed us to be strong at both. We started by learning very basic Python, which involves imperative programming fundamentals, like data structures and different ways of storing data. Next we moved on to SQL, which is vital — I use SQL every single day on the job. 

Then we dove into a wide range of tools that are in demand right now, such as: DBT, Fivetran, AWS, Snowflake. Overall, we learned a host of languages and tools that companies are currently using and would be impressed if you knew. 

Did the teaching style at Jigsaw Labs match your learning style?

It's obvious that Jeff’s passion is teaching. He puts in effort to adjust to and support his students and will dive into problems (such as debugging your code) for however long it takes. Jeff is a patient teacher and I never had any kind of friction with the way he taught; it was totally fluid for me.

I was amazed to see that Jeff weekly iterates on the curriculum to reflect the requirements of current job listings. I was confident that what I was learning was relevant to the modern workforce and that I was developing hireable skills.

What kinds of projects did you work on in the bootcamp?

In general, there were so many projects — we did three large labs a week! The amazing thing is that Jeff has written all of it — He has handwritten 2,500 pages of curriculum for this bootcamp!

The data pipeline is a fundamental concept in data engineering. We built a lot of pipelines, connecting sources, the ETL (extract, transform, load) and all the aspects of that (extracting, transforming, loading, breaking down, etc). We also used a lot of Flask in terms of APIs, back end, and front end. 

Jigsaw Labs taught us the entire structure of a data-based application and a data-intensive application. Together, we took it apart and put it back together. We learned exactly how every segment of a data-intensive application works.

Were there group projects, too?

Jigsaw Labs was very collaborative! We got split into breakout rooms, where we got to work alongside others in this environment, doing this exact line of work, which was an incredible opportunity to learn to work with others and not be stuck in our own heads, scared to ask questions. We had to communicate with our partner to get the job done.  

Tell us about your part-time internship experience at Jigsaw Labs! Which company did you work with for your externship?

Companies sign up with Jigsaw Labs, agreeing to take on cohort members, and give them access to their data engineering team and their projects. It’s a 3-month, part-time internship for up to 20 hours a week, and we spent time in class preparing for it. It was a cool opportunity to be part of their team to get in the vital experience.

My internship was with Cred Protocol, a startup in San Francisco that focuses on the web3 crypto blockchain world. Essentially, they are creating products for decentralized finance. It's such a new space for almost everybody!

The internship is set up so that interns work on projects that include nice-to-have features for their programs, though not necessarily vital tasks to the organization. This means that as an intern, you can safely make mistakes and learn from them. It's intended to be low pressure so you can continue learning.

You’re now a Data Engineer at Cred Protocol! How did you move from intern to data engineer at Cred Protocol?

I interned for six weeks diving into DeFi, a field that was brand new to me. The Head of Data Engineering would communicate tasks to me, and then I would research every new term/tool/mechanism/library. I would think to myself: "would an aeronautical engineer find a handful of leftover screws which should have been inside a just-built jet engine and shrug?" (Hopefully) no. I tried to learn as much new information about the field as possible. Eventually, this research turned into valuable domain knowledge, and soon I was able to assist the company in ways I was unable to previously. 

Even before Cred offered me a position, I had decided to remain in the industry, since I found blockchain data engineering so deep and fulfilling. I put out some feeler applications and inquiries to projects and startups within DeFi listing my domain knowledge and heard back with some degree of interest from all of them. In that way, the internship gave me the opportunity to launch into a new field. That being said, the team at Cred Protocol is so supportive and collaborative, so I was ecstatic to get an offer from them first. 

What kind of projects are you working on now as a data engineer? 

I'm working under the Head of Data Engineering. It's been a great experience, where every day I build something new! The company is currently in the heavy engineering/development phase, so we're producing their product. This includes a lot of ETL to provide Machine Learning engineers with the tools they need to do their work.

What does a typical day look like for you as a data engineer?

  • We start every day with a quick standup. Some days we'll take a longer standup to go more in-depth on issues people are having.
  • We're on Slack all day doing huddles (voice calls), handing off CSV files to each other, and pointing out problems in the code. It's a huge collaborative effort!
  • The hours fluctuate based on the progress of the assignment. Sometimes a task takes 2 or 12 hours and you just have to stick with it until it’s done. 

Are you using everything you learned at Jigsaw Labs?

I'm using these tools that Jigsaw Labs taught me with Python and SQL, but now I'm learning how to layer on top of it. I'm using data previously unknown to me (blockchain data centered around DeFi tokens), but my SQL and Python skills have made the transition painless. I've applied almost everything that Jigsaw Labs taught us in one way or another. Every day I run into tiny snags in the code that I credit Jeff for teaching me, things that if I hadn't known would take me so much longer.

Which soft skills are the most important on the job as a data engineer?

  • Communication. This is a big one. You have to communicate clearly and effectively, especially in a remote environment. Be direct and ask clarifying questions when you're unsure. 
    • Pro Communication Tip! Respond to questions with clarifications. Once both parties are certain of what's required, they can move forward with clarity and confidence. 
  • Accountability. Keeping the right level of accountability between people so that everyone knows who's responsible for what. 

What are the benefits of working at a startup for your very first data engineering role versus a more established company?

If you really want to learn how to be a well-rounded, great data engineer who knows how to build things, I think working at a startup is the way to go. I hear about data engineering jobs at bigger companies where they're only maintaining pipelines that other people have built and reading logs all day. If I did that as my first data engineer job, I wouldn't learn or expand on my skills. Working at a startup now, I'm learning how to build everything, and then if I want a situation where I just maintain things, I could do that later down the road.

Looking back on this experience, was Jigsaw Labs worth it for you? 

There's no doubt that Jigsaw Labs was worth it. The return on investment is insane. I have a job six months after signing up for the bootcamp — It's almost unbelievable. Jigsaw Labs was absolutely worth it. Things worked out better than I could have ever imagined!

What advice do you have for incoming Jigsaw Lab students on how to make the most of their experience?

I got ahead by reading as much as I could about data engineering. Aside from everything we did in class, I would spend about an hour a day researching the subject, exploring the data engineering Reddit page to see what people are saying about the industry, what tools they're using, and what problems they're running into. I read Medium articles about the latest on SQL. Whatever you can do to keep the vocabulary in your head. Keep yourself well-versed on the materials and the tools that data engineers are using. If you keep that in your vocabulary, I think you'll easily pick up everything.

Find out more and read Jigsaw Labs reviews on Course Report. This article was produced by the Course Report team in partnership with Jigsaw Labs.

About The Author

Jess is the Content Manager for Course Report as well as a writer and poet. As a lifelong learner, Jess is passionate about education, and loves learning and sharing content about tech bootcamps. Jess received a M.F.A. in Writing from the University of New Hampshire, and now lives in Brooklyn, NY.

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