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Things To Know Before Starting a Career in Data Engineering

Among the top emerging jobs are machine learning, data science, and big data engineering. Many people are getting a high salary in a big data carrier. First, you need to know that a data science degree is not something like training in data engineering.

You need to know that data science is something that is heavily math oriented. Data engineering work is generally based on the tech side, building data pipelines, etc. if you are working in big data, you often need a big team. It generally works with people who have the roles such as data warehouse engineer, data platform engineer, data infrastructure engineer, analytics engineering, data architecture, and Devops engineer.

It helps to make students and mid-career professionals understand whether data engineering is for them or not. Here you have some of the tips and things you should know before starting a career in data engineering.

  • If the Role You Are Choosing Is Too High, Then Try Another Data Role

The first job as a data engineer may be challenging for you because it depends only on your experiences. If software engineering is new to you, then you have a higher technical barrier. Data engineering requires a high range of technical skill sets, and it is much wider than data analysis. Therefore you can start with data analysis is a good start for you as it needs fewer hard skills and more domain knowledge. A data analyst mostly works with data engineers. So that you get many opportunities to make you understand what they do and you may ensure that what time you are ready to apply for this. It helps you to go through less hard work at first.

  • Do Not Start With Streaming and Machine Learning

Never depend on the buzzwords you find in the job offers. If you depend on the company’s data maturity, then sometimes you must not have these concepts. It was mentioned last year that only 9% of the people get hired from machine learning. Many companies are struggling a lot with basic data engineering, whereas AI adoption is growing fast. If you are a junior, then there is a baseline on the basic knowledge that helps to cover the use cases and helps you to get pretty far. You can cover most of the analytical batch if you know how to solve the python and SQL by using the classic framework.

  • Basics of Software Engineering Matter a Lot

You should always remember that data engineering is very similar or just another type of software engineering. Nowadays, data engineering comes from non-software engineering backgrounds, such as BI developers and data analysts. And if you completed a master’s in these basics, then you can get chances among the software engineer peers. And you can also get to understand the edge of how to deliver a production-ready project.

  • Learn Things End to End

In this, you can get a chance to learn and build things end to end with the help of side projects. You need to design the data from point A, transform, and consume it, and you have to make a decision on the basis of this. Suppose you can have a good picture and understand how each component can talk to each other. Then this is the best place for you; you can easily get chances by making your skill set into an actionable value.

  • Focus On One Cloud Provider

Many of the cloud providers have similarities in the basis of tooling. They use fancy names to attract you. You have to just select one provider and ensure that what is the equivalent of service you are using on another cloud provider. Sometimes you find some differences in significant features. Then you should need to grasp how the tool can fit you and can help you to get end-to-end knowledge without having the experience.

  • Always Target Young Companies

You should always focus on the companies that are cloud-native, and as a junior, you can get a lot of help from this. Firstly, you likely have spent already focusing on cloud service. If the company where you want to join has the old framework, then this will help you to get additional knowledge. After that, you should always ensure that the time invested in learning the data process in the modern stack will last for at least before becoming obsolete.

  • You Must Be a Strong Developer

You should always need to develop your skills in data engineering. The data engineer, Anderson, says that he didn’t stress about how important it is for data engineering to have a strong programming background. They should always need an interest in data, like finding patterns in data. If they did not have any interest in the data, then it would be boring for them. Also, they need to have the ability to solve difficult or complex projects. Big data is generally ten times more complicated than small data.

  • Experience

In this field, you need to have a lot of experience. You need to have a lot of skills that develop and be a site reliability engineer, which overlaps with data engineering and the responsibilities. A large amount of skill you need to pick yourself. As well as the data engineer has to migrate and transform them so that it helps make sense of data to data scientists and data analysts. Most of the time, the teams of data engineering skew toward senior people, Anderson said.

Wrapping Up

Here you can get a lot of things and tips before you join the data engineering field. These tips can help you to understand whether this data engineering field is suitable for you or not.

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