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Cracking the System Design Interview for Data Engineers.

ALEXEY BANAEV

Data platform interviews test CDC, streaming, warehouse modeling, and trade-offs two levels deep — no generic system-design book covers it.

This one does: 50 full mock interviews, 122 rapid-fire Q&A, and the framework that turns a vague prompt into a hire.

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About

About

About the Book

Every senior data engineer interview eventually arrives at the same terrifying sentence: “Design a data platform for us.”

No whiteboard trick, no LeetCode grind, and no generic system-design book — built for backend engineers designing URL shorteners — will save you.

Data platform interviews test a different game: CDC and exactly-once semantics, Star vs. Data Vault, Kafka vs. Event Hub, watermarks, backfills, and the trade-off you must defend two levels of “why?” deep.

Cracking the System Design Interview for Data Engineers is the book that game deserves.

Inside: a universal response framework you can run on any prompt in under a minute; deep, interview-shaped dives into every system class you’ll be asked to design — lakehouses, CDC platforms, streaming and batch pipelines, warehouse modeling, incremental loading, security, cost, performance, disaster recovery, governance, and the Azure/Fabric/Databricks ecosystem; the nine hardest company-style prompts (Netflix, Uber, fraud detection, banking, healthcare, and more); a canonical registry of the ten trade-offs interviewers ask about most; 122 rapid-fire follow-up questions with model answers; and 50 full mock interviews — complete with follow-up chains, constraint curveballs, and four dissected failure transcripts that show you exactly what losing the room looks like.

But treat the interview framing as the entry point, not the ceiling. Every chapter is built the same way a real architecture decision is made — the requirements that actually matter, the two or three options a senior engineer would seriously weigh, the trade-offs behind each one, and the conditions under which the “right” answer flips. That is not interview trivia; it is the reasoning you reach for the next time a stakeholder asks “why Kafka and not Event Hub,” a customer wants to know why their warehouse should look like a star schema instead of a vault, or your own team is debating batch versus streaming for the third time this quarter. Keep it on the shelf as a working reference: before your next architecture review, re-read the relevant trade-off and walk in with the criteria already framed, not improvised on the whiteboard.

This isn’t a book you read once. It’s a book you rehearse with before interviews — out loud, with a timer, until the framework becomes reflex — and reach for again on the job, every time a real design decision is on the table.

You already know more than you think. This book will help you prove it — in the interview room, and in the next design review that actually matters.

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Author

About the Author

ALEXEY BANAEV

Alexey Banaev is a Lead Data Engineer and Data Architect with more than 20 years of experience in data warehousing, business intelligence, and cloud data platforms. He specializes in Microsoft Azure, Microsoft Fabric, Databricks, Data Vault 2.0, and modern Lakehouse architectures.

Throughout his career, Alexey has designed and delivered enterprise-scale data platforms, led distributed engineering teams, and helped organizations modernize their data ecosystems. He is passionate about data modeling, AI-powered data solutions, knowledge sharing, and mentoring fellow engineers.

Alexey writes about data engineering, architecture patterns, AI applications in data platforms, and practical lessons learned from real-world projects.

Contents

Table of Contents

Preface

  1. What this book is about
  2. Why I made it
  3. What makes this book different
  4. What this book is not — and what it accidentally became
  5. How to use this book

Chapter 1. How the System Design Interview Works

  1. 1.1 What Is Being Evaluated
  2. 1.2 Anatomy of the 45–60 Minute Session
  3. 1.3 How to Lead the Dialog
  4. 1.4 Mistakes Senior Candidates Make
  5. 1.5 Senior vs Staff Signals
  6. 1.6 How to Answer in a Structured Way
  7. 1.7 Meta-Questions Interviewers Ask — with Model Answers
  8. 1.8 Trade-offs in How You Answer
  9. 1.9 Typical Mistakes in This Chapter’s Territory
  10. 1.10 How to Answer Confidently

Chapter 2. The Answer Framework

  1. 2.1 Step 1 — Clarify Requirements (~4 min)
  2. 2.2 Step 2 — Functional Requirements (~2 min)
  3. 2.3 Step 3 — Non-Functional Requirements (~4 min)
  4. 2.4 Step 4 — High-Level Architecture (~8 min)
  5. 2.5 Step 5 — Storage (~5 min)
  6. 2.6 Step 6 — Data Flow (~5 min)
  7. 2.7 Step 7 — Compute (~4 min)
  8. 2.8 Step 8 — Security (~2 min)
  9. 2.9 Step 9 — Scalability (~3 min)
  10. 2.10 Step 10 — Reliability (~3 min)
  11. 2.11 Step 11 — Cost (~2 min)
  12. 2.12 Step 12 — Monitoring (~2 min)
  13. 2.13 Step 13 — Trade-offs (~3 min)
  14. 2.14 The One-Page Cheat Sheet
  15. 2.15 The Framework Applied: a Small Worked Example
  16. 2.16 Adapting the Framework
  17. 2.17 Q&A: Defending the Framework Itself
  18. 2.18 Trade-offs of the Framework
  19. 2.19 Common Mistakes with the Framework

Chapter 3. Design a Data Platform

  1. 3.1 Theory
  2. 3.2 Architecture Diagrams
  3. 3.3 Interviewer Questions
  4. 3.4 Ideal Candidate Answer — Worked in Full
  5. 3.5 Tricky Follow-up Questions
  6. 3.6 Trade-offs
  7. 3.7 Common Mistakes
  8. 3.8 How to Answer Confidently

Chapter 4. Design a Lakehouse

  1. 4.1 Theory
  2. 4.2 Architecture Diagrams
  3. 4.3 Interviewer Questions
  4. 4.4 Ideal Candidate Answer — Worked in Full
  5. 4.5 Tricky Follow-up Questions
  6. 4.6 Trade-offs
  7. 4.7 Common Mistakes
  8. 4.8 How to Answer Confidently

Chapter 5. Design a CDC Platform

  1. 5.1 Theory
  2. 5.2 Architecture Diagrams
  3. 5.3 Interviewer Questions
  4. 5.4 Ideal Candidate Answer — Worked in Full
  5. 5.5 Tricky Follow-up Questions
  6. 5.6 Trade-offs
  7. 5.7 Common Mistakes
  8. 5.8 How to Answer Confidently

Chapter 6. Design a Streaming Platform

  1. 6.1 Theory
  2. 6.2 Architecture Diagrams
  3. 6.3 Interviewer Questions
  4. 6.4 Ideal Candidate Answer — Worked in Full
  5. 6.5 Tricky Follow-up Questions
  6. 6.6 Trade-offs
  7. 6.7 Common Mistakes
  8. 6.8 How to Answer Confidently

Chapter 7. Design a Batch Platform

  1. 7.1 Theory
  2. 7.2 Architecture Diagrams
  3. 7.3 Interviewer Questions
  4. 7.4 Ideal Candidate Answer — Worked in Full
  5. 7.5 Tricky Follow-up Questions
  6. 7.6 Trade-offs
  7. 7.7 Common Mistakes
  8. 7.8 How to Answer Confidently

Chapter 8. Design Metadata-driven ETL

  1. 8.1 Theory
  2. 8.2 Architecture Diagrams
  3. 8.3 Interviewer Questions
  4. 8.4 Ideal Candidate Answer — Worked in Full
  5. 8.5 Tricky Follow-up Questions
  6. 8.6 Trade-offs
  7. 8.7 Common Mistakes
  8. 8.8 How to Answer Confidently

Chapter 9. Design a Data Warehouse

  1. 9.1 Theory
  2. 9.2 Architecture Diagrams
  3. 9.3 Interviewer Questions
  4. 9.4 Ideal Candidate Answer — Worked in Full
  5. 9.5 Tricky Follow-up Questions
  6. 9.6 Trade-offs
  7. 9.7 Common Mistakes
  8. 9.8 How to Answer Confidently

Chapter 10. Design Incremental Loading

  1. 10.1 Theory
  2. 10.2 Architecture Diagrams
  3. 10.3 Interviewer Questions
  4. 10.4 Ideal Candidate Answer — Worked in Full
  5. 10.5 Tricky Follow-up Questions
  6. 10.6 Trade-offs
  7. 10.7 Common Mistakes
  8. 10.8 How to Answer Confidently

Chapter 11. Design Data Quality

  1. 11.1 Theory
  2. 11.2 Architecture Diagrams
  3. 11.3 Interviewer Questions
  4. 11.4 Ideal Candidate Answer — Worked in Full
  5. 11.5 Tricky Follow-up Questions
  6. 11.6 Trade-offs
  7. 11.7 Common Mistakes
  8. 11.8 How to Answer Confidently

Chapter 12. Security

  1. 12.1 Theory
  2. 12.2 Architecture Diagrams
  3. 12.3 Interviewer Questions
  4. 12.4 Ideal Candidate Answer — Worked in Full
  5. 12.5 Tricky Follow-up Questions
  6. 12.6 Trade-offs
  7. 12.7 Common Mistakes
  8. 12.8 How to Answer Confidently

Chapter 13. Monitoring

  1. 13.1 Theory
  2. 13.2 Architecture Diagrams
  3. 13.3 Interviewer Questions
  4. 13.4 Ideal Candidate Answer — Worked in Full
  5. 13.5 Tricky Follow-up Questions
  6. 13.6 Trade-offs
  7. 13.7 Common Mistakes
  8. 13.8 How to Answer Confidently

Chapter 14. Cost Optimization

  1. 14.1 Theory
  2. 14.2 Architecture Diagrams
  3. 14.3 Interviewer Questions
  4. 14.4 Ideal Candidate Answer — Worked in Full
  5. 14.5 Tricky Follow-up Questions
  6. 14.6 Trade-offs
  7. 14.7 Common Mistakes
  8. 14.8 How to Answer Confidently

Chapter 15. Performance

  1. 15.1 Theory
  2. 15.2 Architecture Diagrams
  3. 15.3 Interviewer Questions
  4. 15.4 Ideal Candidate Answers — Both Worked
  5. 15.5 Tricky Follow-up Questions
  6. 15.6 Trade-offs
  7. 15.7 Common Mistakes
  8. 15.8 How to Answer Confidently

Chapter 16. Disaster Recovery

  1. 16.1 Theory
  2. 16.2 Architecture Diagrams
  3. 16.3 Interviewer Questions
  4. 16.4 Ideal Candidate Answer — Worked in Full
  5. 16.5 Tricky Follow-up Questions
  6. 16.6 Trade-offs
  7. 16.7 Common Mistakes
  8. 16.8 How to Answer Confidently

Chapter 17. Data Governance

  1. 17.1 Theory
  2. 17.2 Architecture Diagrams
  3. 17.3 Interviewer Questions
  4. 17.4 Ideal Candidate Answer — Worked in Full
  5. 17.5 Tricky Follow-up Questions
  6. 17.6 Trade-offs
  7. 17.7 Common Mistakes
  8. 17.8 How to Answer Confidently

Chapter 18. Fabric Architecture

  1. 18.1 Theory
  2. 18.2 Architecture Diagrams
  3. 18.3 Interviewer Questions
  4. 18.4 Ideal Candidate Answer — Worked in Full
  5. 18.5 Tricky Follow-up Questions
  6. 18.6 Trade-offs
  7. 18.7 Common Mistakes
  8. 18.8 How to Answer Confidently

Chapter 19. Azure Architecture

  1. 19.1 Theory
  2. 19.2 Architecture Diagrams
  3. 19.3 Interviewer Questions
  4. 19.4 Ideal Candidate Answer — Worked in Full
  5. 19.5 Tricky Follow-up Questions
  6. 19.6 Trade-offs
  7. 19.7 Common Mistakes
  8. 19.8 How to Answer Confidently

Chapter 20. 100+ Follow-up Questions

  1. 20.1 Streaming & Messaging (14)
  2. 20.2 Lakehouse & Table Formats (14)
  3. 20.3 CDC & Ingestion (12)
  4. 20.4 Orchestration & Batch (10)
  5. 20.5 Modeling & Data Warehouse (12)
  6. 20.6 Incremental Loading (8)
  7. 20.7 Data Quality (8)
  8. 20.8 Security & Governance (10)
  9. 20.9 Performance (8)
  10. 20.10 Cost (6)
  11. 20.11 Reliability & DR (6)
  12. 20.12 Platform, Azure & Fabric (8)
  13. 20.13 Meta & Behavioral (6)
  14. 20.14 How Interviewers Chain These — and How to Survive It

Chapter 21. The Hardest Questions

  1. 21.1 Netflix — Viewing Analytics & Personalization Data Platform
  2. 21.2 Uber — Trip & Marketplace Analytics Platform
  3. 21.3 Amazon — Order & Inventory Analytics (Retail DWH)
  4. 21.4 Spotify — Listening Analytics & Royalty Platform
  5. 21.5 Banking Platform — Regulatory & Analytical Data Platform
  6. 21.6 Fraud Detection — Real-Time Scoring Data Platform
  7. 21.7 IoT Platform — Telemetry Ingestion & Analytics
  8. 21.8 Financial DWH — Enterprise Finance Warehouse
  9. 21.9 Healthcare Platform — Clinical & Claims Data Platform
  10. 21.10 Common Mistakes Across All Nine
  11. 21.11 How to Answer Confidently

Chapter 22. Trade-offs

  1. 22.1 How to Talk About Trade-offs
  2. 22.2 Batch vs Streaming
  3. 22.3 ADF vs Databricks
  4. 22.4 Snowflake vs Fabric
  5. 22.5 Fabric vs Databricks
  6. 22.6 Lakehouse vs Warehouse
  7. 22.7 Star Schema vs Data Vault
  8. 22.8 SQL vs Spark
  9. 22.9 Delta vs Iceberg
  10. 22.10 Event Hub vs Kafka
  11. 22.11 Synapse vs Fabric
  12. 22.12 The Two Most-Chained Families, as Decision Trees
  13. 22.13 Building a Matrix Live
  14. 22.14 Common Mistakes
  15. 22.15 How to Answer Confidently

Chapter 23. 50 Mock Interviews

  1. Block I — Platform & Lakehouse (Mocks 1–10)
  2. Block II — CDC & Ingestion (Mocks 11–17)
  3. Block III — Streaming (Mocks 18–25)
  4. Block IV — Batch & Orchestration (Mocks 26–31)
  5. Block V — Modeling & DWH (Mocks 32–37)
  6. Block VI — Cross-Cutting & Case Studies (Mocks 38–50)
  7. 23.51 Closing: How to Use Fifty Mocks

Chapter 24. Typical Candidate Mistakes

  1. 24.1 The Catalog
  2. 24.2 The Mistake Map
  3. 24.3 The Night-Before Checklist
  4. 24.4 After a Bad Answer

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