Data Engineering Interview Questions: Python, SOLID, and Code Quality I

We keep talking about data engineering interviews this part number four, and the first part in Python sections. So let’s use here a little intro before going directly into interview questions, shall we? Python, with its readability and flexibility, has become a must language for orchestrating ETL pipelines, managing complex data transformations, and tying it all together with error handling. When married with foundational concepts like SOLID, DRY, and KISS, you create a recipe for code that’s both powerful and useful. In this section, you’ll explore the core questions that reveal how ready you are to thrive in a demanding data engineering role.

Why do you claim Python is so popular, and why are you so passionate about it?
Because Python’s readability is like a breath of fresh air in a world full of cryptic code and hidden logic. I love how it practically forces developers to think about clarity and structure. Unlike some languages that bury you in rigid syntax or meaningless brackets, Python invites you to focus on what truly matters: the flow of data and the intent of your functions. This alone makes it an ideal vehicle for teaching best practices. Whether you’re building a tiny script or a colossal enterprise app, you can’t escape Python’s push toward simplicity. And that simplicity is the gateway to mastering deeper concepts like SOLID, DRY, and KISS. These principles, once learned through Python’s lens, become second nature in any coding environment.