Data Engineering & AI Interview Playbook

Take your SQL skills beyond the basics with practical examples covering joins, subqueries, CTEs, set operations, date and string functions, conditional aggregation, and filtering with EXISTS or IN. These patterns are used every day in data pipelines, warehouse transformations, analytics models, and technical interviews. Work through the examples and try the practice questions to build confidence. In this chapter Sample datasets INNER, LEFT, RIGHT and FULL JOIN Subqueries and Common Table Expressions (CTEs) UNION vs. UNION ALL Date and string functions Conditional aggregation EXISTS vs. IN Interview practice Sample datasets used in this chapter We’ll use two small tables: customers and orders . customers customer_id customer_name city 1 Alice Bangalore 2 Bob Mumbai 3 Charlie Delhi 4 Diana Pune orders order_id customer_id amount order_date status 101 1 1200 2026-01-05 Completed 102 1 800 2026-01-15 Pending 103 2 2000 2026-02-02 Completed 104 5 500 2026-02-1...

Master the fundamentals of SQL with practical examples designed for Data Engineering interviews. In this chapter, you’ll learn essential SQL concepts from filtering and aggregating data to handling NULL values, applying conditional logic, and converting data types using real-world examples . By the end, you’ll have a strong foundation to solve SQL interview questions with confidence. In this chapter Sample dataset SELECT, WHERE, ORDER BY, DISTINCT GROUP BY and HAVING Aggregate functions NULL handling CASE WHEN Data types and type casting Interview practice Sample dataset used throughout this chapter Imagine you’re a Data Engineer working for an e-commerce company. You have an orders table containing customer purchases. order_id customer amount status city 101 Alice 1200 Completed Bangalore 102 Bob 800 Pending Mumbai 103 Alice 1500 Completed Bangalore 104 Charlie NULL Cancelled Delhi 105 Bob 2000 Completed Mumbai 106 David 800 Pending NULL We’ll use thi...

  Why Debugging Matters More Than Building Pipelines In production systems, pipelines rarely fail cleanly. More often, they succeed with incorrect data , which is far more dangerous. Most senior Data Engineering interviews today include debugging scenarios , not just “how would you build X”. Below are real situations Data Engineers face—and how to debug them correctly. Scenario 1: Pipeline Succeeded, but Dashboard Numbers Are Wrong Problem A daily pipeline ran successfully, but: Revenue numbers are inflated User counts are higher than expected No job failures or alerts Common Root Causes Duplicate ingestion Incorrect joins Missing deduplication Late-arriving data processed twice How to Debug Compare row counts between raw and transformed tables Check if data for the same date was ingested more than once Validate join keys (many-to-many joins are common culprits) Check incremental logic (e.g., updated_at filters) Example Fix If d...

  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...