Data Engineering & AI Interview Playbook

  1. Why GenAI Matters to Data Engineers (Not Just ML Engineers) Generative AI systems are no longer experimental add-ons; they are becoming first-class consumers of data platforms . While ML Engineers focus on model selection and training, Data Engineers are responsible for the data foundations that make GenAI systems reliable, scalable, and trustworthy . So Data Engineering acts as strong foundation for GenAI systems. From chatbots to internal AI assistants, GenAI applications depend heavily on: Clean, well-structured data Reliable ingestion pipelines Low-latency access to relevant information This means Data Engineers do not need to become ML experts—but they must understand how their data systems support AI workflows . 2. What Data Engineers Do NOT Need to Know Let’s clear a common misconception. Data Engineers are not expected to: Train large language models Tune neural network hyperparameters Implement backpropagation or transformers Compete with ML Engineers or researchers...

Decorators provide additional functionality to the functions without directly changing their definition of them. Basically, it takes the functions as an argument, adds functionality to them, and returns it.  Before diving deep into the concept of Decorators, let's first try to understand  What are functions in Python and what is an inner function? In Python everything is Objects, be it Class, Variables, and Functions . So functions are python first-class objects that can be used or passed as an argument. You can store the functions in variables, you can pass a function to another function as parameters, and you can also return the function from the function. Below is one simple example where we are treating functions as objects . def make_me_lowercase ( str ): return str .lower() print (make_me_lowercase( "HELLO World" )) copy_of_you = make_me_lowercase print (copy_of_you( "HELLO World" )) The output of the above calls for both the functio...

[ Updated for 2026] : [This article was originally written in 2022 and has been revised to reflect current Data Engineering trends.] Why SQL Still Matters for Data Engineers in 2026 Despite the rise of modern data stacks, cloud platforms, SQL remains one of the most critical skills for Data Engineers . In fact, SQL is often the first technical filter in Data Engineering interviews across startups, product companies, and large enterprises. Generally, there is a perception that SQL interviews are only for Data Analysts or Business Analysts but that's not the case. Writing SQL are essential part of Data Engineering specially for ELT pipelines that involves dbt. In 2026, SQL interviews are no longer about memorizing syntax. Interviewers focus on: How you think in SQL How you model and transform data How you handle scale, performance, and correctness How SQL supports analytics, reporting, and AI pipeline In this post, we are going to provide the frequently asked  SQL ...

Updated for 2026 This article was originally written in 2022 and has been revised to reflect modern Data Engineering interview expectations in 2026 , including cloud-native pipelines, SQL-heavy roles, and AI-ready data systems. In my previous post How to Prepare for Data Engineer Interviews (2026) , I discussed a structured approach to interview preparation. In this post, I cover frequently asked basic Data Engineering interview questions with brief answers . 👉 These questions are typically asked in early interview rounds , where interviewers want to assess: Conceptual clarity Practical understanding Ability to reason, not memorize You are not expected to go deep in these rounds — clarity matters more than depth. A. Programming (Python for Data Engineers) 1. What is a static method in Python? A static method is bound to a class rather than an instance. It does not access instance variables and can be called using the class name. Static methods are commonly used for utilit...

[ Updated for 2026] : [This article was originally written in 2022 and has been revised to reflect current Data Engineering and AI interview trends.] In recent years, the exponential growth of data—driven by cloud adoption, digital products, IoT, and AI systems has made data a core business asset for almost every organization. As a result, companies across industries are heavily investing in data platforms to enable analytics, real-time insights, and AI-driven decision-making. This shift has significantly increased demand for Data Engineering roles, and Data Engineers continue to be among the most in-demand and strategically critical profiles in the IT industry. By 2026, the role of a Data Engineer has evolved beyond traditional ETL development. Organizations now expect Data Engineers to design scalable, reliable, and AI-ready data platforms that can support analytics, machine learning, and Generative AI use cases. Modern data teams rely on Data Engineers to build robust ingestion p...