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10 Reasons Why Aspiring Data Engineers Choose Pipeline Academy

Ambitious data analysts, data scientists trying to take their careers to the next level, product owners aiming to build next-generation data products, software engineers dealing with legacy data stacks... they all are facing the same challenge: how do I get the data engineering skills that enable me to achieve my goals?

This frustration is very real, and it is indeed very common! Plenty of professionals are trying to find a simple and well-structured leaning path for data engineering, but the road is paved with deceptive marketing messages and fake experts.

The students who ended up at Pipeline Academy gave us their reasons for picking us, and this list was born. You can read their reviews on CourseReport. So if you feel that you can identify with any of the below, just schedule a call and have a chat with me!

1. Online courses don’t work for most

Raise your hand if you've finished every online MOOC course you've started. Trust me, you are not alone. It's difficult to filter for the meaningful platforms/courses that deliver value especially when it comes to learning data engineering, but even if you find them (here are some curated suggestions), bringing up the right amount of motivation to finish them can get really tough. Our bootcamp delivers the structure, the methodology and the environment that makes you achieve your goals.

2. Issues and frustration when building data infrastructures

So you are the data person who somehow ended up being the one building, improving and maintaining the data infrastructure at your company. Yet you lack the proper training to make informed decisions about tooling and optimisations? Or you are being held back from implementing your ideas, concepts and models due to the lack of data engineers or infra know-how? This is the thing that leads data analysts and data scientist leave their jobs and look for a more fulfilling path within the realm of data, which we know is more than achievable.

3. Career improvement and salary increase

Many data pros want to get more technical and earn the benefits of doing so (promotions, more job opportunities, higher salary etc.), and realise that a compact yet intensive course would be the right path forward, covering the fundamentals that apply regardless of the industry they are in. When you are stuck at your job as BI analyst, data scientist or product person, improving your technical skillset delivers a high return on investment even in the short term.

4. No fluff, no bs, just straight-talk

Hard facts corroborated by benchmarks, hands-on experience with using tools and approaches at large organisations, and a contrarian yet truthful opinion are not easy assets to come by as a learner when players in the data ecosystem are hard-pressed to avoid them. Branded courses with marketing-as-a-curriculum (aka MaaC) will misguide learners and give them the warm and cozy feeling of certainty. This is the best tool, stop asking questions. We just do the opposite — in all of our work.

5. Data engineer job descriptions can be intimidating

Have you ever read a job description for a data engineer role? Did it read like the line-up of an indie music festival (Great Expectations, ClickHouse etc.), or the roster of an Italian football team (Luigi, Apache Cassandra etc.) or something Elon Musks PR team came up with as a grandiose joke (Terraform, Kinesis Firehose etc.)? If your answer is yes, I have news for you: this is the exact reason why even experienced data folks are often hesitant to apply for these positions. Part of the measurable transformation (before vs. after) that results from doing the course is that you'll feel empowered to apply, and you'll have the confidence to rock the interviews. Right, Michele?

6. Get a holistic & structured view on the data infrastructure ecosystem

In order to get ready to start as data engineer after 12 weeks of training, you need to develop an understanding of the fundamental concepts, of the tooling landscape, of best practices, but also of the surrounding business context. Clouds and virtualisation? Real-life coding challenges from renowned organisations? Recommendations for diving deeper into ML, data modeling and dataops? It's all in our curriculum.

7. Small cohorts and personalised learning experience

Some franchise bootcamps received significant backlash after filling their virtual classrooms with 30-40 students, which rendered organic teacher-learner and learner-learner interactions difficult. Active participation and asking questions should be encouraged all the time, this is how a coding bootcamp experience is supposed to be more than just looking at way too many talking heads in a Zoom window, where you are more or less just a number. Keeping the cohorts deliberately small allows us to focus on individual needs, customise career coaching and deliver an outstanding learning experience.

8. Expert teachers and renowned guest speakers

Daniel and I have done our fair share of shenanigans in the realm of tech, data, education and building teams and digital products. We've made loads of mistakes along the way, and we love to share our war stories so you don't end up repeating them. Our guest speakers share this attitude, and the opportunity to pick their brain is not something you'll get in any other school or conference.

9. Future-proof knowledge

Nobody really knows what the data infrastructure landscape is going to look like in 2030. Yet there are timeless best practices and tools attached to the smart data engineer's belt that allow for dealing with new and shiny trends of the day. There is a reason why we turn to SQL, Python and Makefiles in 2021, and there are reasons why we apply concepts like TCO before making decisions on tooling.

10. Solving Real problems and building new Products is rewarding

It's not easy to stand out from the job seeking masses by doing the same pre-defined exercises as all the others. What would you say if I told you that the assignments at Pipeline Academy are designed in a technology-agnostic and solution-oriented fashion? We care about helping you figure things out rather than giving you 'fill in the blanks' type of assignments. If you like puzzles, you'll love the course.