Data engineering is a challenging field and thinking about how to organize a project can pay big dividends. Learn best practices for data engineers here.

If you are trying to improve your data engineering skills or are the sole data person in your company, it can be hard to know how well your technical skills are developing. Questions like Am I building pipelines the right way? How do I measure up to DEs at bigger tech companies? How do I get feedback on my pipeline design? It can cause a lot of uncertainty in career development! Imagine if you know that your code is on par (or even better than) with pipelines at tech-forward companies and that you are using industry best practices. You will be confident with your career progression and can quickly ramp up on any code base. These industry-standard best practices and concepts required to build resilient data pipelines are what you will learn in this post! By the end of this post, you will know the underlying concepts behind best practices and when to use them. While there is no perfect code/design, following these concepts will help you build resilient and easy-to-maintain data pipelines.

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