The Dataquest Download
Level up your data and AI skills, one newsletter at a time.
Hello, Dataquesters!
Here’s what we have for you in this edition:
Top Read: 30+ entry-level data engineering interview questions with code (SQL, Python, ETL, modeling, system design, behavior). Learn more
Webinar Recording: Build a Python AI assistant with LLM function calling (no hallucinations). Watch now
From the Community: Help with Airflow-in-Docker setup; fixes for the Customers & Products SQL project. Join the discussion
What We’re Reading: Analytics careers in 2026, pandas 3.0 changes, and how AI speeds easy tasks but makes deep debugging harder. Learn more
Data engineering interviews can feel intimidating when you don’t know what’s coming. You might be switching careers, coming from a bootcamp, or self-taught, and it’s hard to tell which topics to focus on or what “good answers” actually look like.
This guide covers 30+ entry-level data engineering interview questions with code examples for every technical question, plus clear explanations of what interviewers are really evaluating. You’ll prep across SQL, Python, data modeling, ETL pipelines, system design basics, and behavioral questions, along with the common mistakes that get candidates rejected.
Webinar Recording
Catch the recording and learn how to build a Python-based AI assistant that doesn’t hallucinate. This beginner-friendly walkthrough shows you how to use LLM function calling to connect models with real tools for pricing, inventory lookups, and bulk order calculations. Perfect for anyone curious about how AI agents actually interact with real systems.
From the Community
Configuring the Airflow Project: Mihael is working on running and managing Apache Airflow inside Docker and has encountered a persistent issue during the setup stage—your guidance would be greatly appreciated.
Customers and Products Analysis Using SQL: Alla discusses potential pitfalls related to the guided project on Customers and Products Analysis Using SQL, along with ideas on how to fix them.
What We're Reading
Is Data Analytics Still a Good Career Choice in 2026: AI isn’t killing analyst roles, but it is changing them. This piece explains how analytics work is shifting toward automation, AI collaboration, and stronger business impact.
What’s New in pandas 3.0: A quick overview of pandas 3.0 updates, including copy-on-write memory behavior, removed deprecated features, and cleaner missing data handling (plus what might break in older code).
AI Makes the Easy Part Easier, and the Hard Part Harder for Developers: AI speeds up routine coding, but can slow down deeper work like debugging, reasoning, and validating solutions, increasing cognitive load and burnout risk.
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