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The Data Engineering Interview, Unlocked
Volume 1: Foundations & Platforms
A visuals-first guide to cracking data engineering interviews — SQL, data modeling, PySpark, Spark, Kafka, Databricks and Snowflake — and doing the work in production.
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About
About the Book
Learn it. Scale it. Defend it under questioning.
Data engineering interviews don't just test whether you know a concept. They test whether you can apply it at production scale and explain your trade-offs to a sceptical interviewer. The Data Engineering Interview, Unlocked — Volume 1: Foundations & Platforms is a visuals-first guide built around exactly that.
Inside Volume 1:
- The Interview Framework: how data engineering interviews really work, and how to prepare for each round
- SQL Mastery: from the execution model to cross-platform, FAANG-level SQL
- Data Modeling and Coding: star schemas through production-grade Python and PySpark
- Apache Spark: architecture, execution, performance tuning and debugging
- Kafka and Streaming: internals, reliability, scale and stream processing
- Databricks, Delta Lake and Snowflake: the modern platform from lakehouse to warehouse
- Classified question bank, cheat sheets and glossary for fast revision
Who it's for: aspiring and working data engineers preparing for interviews, and engineers moving into Spark, streaming or lakehouse platforms.
This is Volume 1 of two. Volume 2 — System Design, AI & Career — covers system design case studies, AI for data engineers, behavioral interviews and mock interview loops.
Author
About the Authors
Pankaj jain
Pankaj brings 22 years of technology leadership across fintech, banking and data platforms. He has worked with Bank of America, Boots, Deutsche Bank and Scotiabank across the US, Canada, the UK and Europe, spanning fintech, retail and payments, and has led global delivery teams across India. A Canadian Permanent Resident, he brings a rare combination of global fintech depth and Canadian banking experience, and is drawn to ambitious work at the intersection of AI, fraud and financial data. His leadership is backed by strong credentials across strategy, delivery and hands-on data engineering. He holds an Executive MBA from IIM Kolkata and a Certificate in Generative AI Strategy from the Rotman School of Management, Toronto. He is a Databricks Certified Data Engineer Associate, PMP certified and ITIL Expert certified, which lets him bridge business vision with modern data and AI execution.
Harendra Singh
Harendra Singh is a multi-cloud certified Solution Architect and Data Engineering leader with more than 15 years of experience designing and delivering large-scale Data, Cloud, and AI platforms. His expertise spans data engineering, real-time streaming, lakehouse architecture, cloud modernization, and AI, with hands-on experience across Databricks, Apache Spark, Delta Lake, Apache Kafka, AWS MSK, Snowflake, Python, Scala, Java, and SQL. His work focuses on the engineering decisions that matter at scale—from architecture and performance to reliability, cost, and operational simplicity—with a strong emphasis on building systems that can evolve as data, workloads, and business demands grow. Through his writing, Harendra brings real-world engineering experience to data engineering and system design, exploring the patterns, trade-offs, and architectural decisions behind scalable, resilient, and production-ready systems.
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