Preface xv Foreword xvii Introduction xxi Chapter 1 Growth Engineering 1 The Role of Engineers in Product Growth 2 Key Growth Strategies 3 Habit Formation 3 Freemium Model 4 Experimentation 4 Data-Driven Growth 5 Chapter 2 Observability 7 Instrumentation 9 How to Know What to Instrument 10 Legal and Compliance Checklist 11 A Practical Example of Instrumentation 13 Telemetry 14 Logs 16 Metrics 17 Traces 19 Implementing Observability in Practice 20 Defining the Signals 21 Understanding the Flow 21 Using Observability to Act 22 Making It a Habit 22 Observability Anti-Patterns 22 Tracking Everything Without Purpose 22 Logging Without Context 23 Relying Only on Logs 23 Instrumenting Too Late 23 No Clear Ownership 24 Tools for Observability 24 What This Chapter Covered 27 Key Questions for Reflection 27 Exercise 27 Chapter 3 Data Pipelines 29 What Is a Data Pipeline and Why Does It Matter? 29 Components of a Data Pipeline 31 Ingestion 31 Batch Ingestion 31 Streaming Ingestion 32 Transportation 33 Message Brokers or Queues 34 Streaming Platforms or Distributed Logs 34 Telemetry Forwarders or Data Shippers 34 Processing 35 Keep It Simple at First 36 Validate Early 37 Make It Observable 37 Use Version Control for Logic 38 Storage 39 Data Warehouses 39 Data Lakes 39 When to Use What 40 Visualization 40 Tools and Interfaces 41 Types of Visualizations and When to Use Them 42 Building a Growth Pipeline with Large Language Models 46 Step 1: Define the Role or Persona 47 Step 2: Define What You Want to Measure 48 Step 3: Instrumentation Strategy 48 Step 4: Generate Mock Data 49 Step 5: Process Data 50 Step 6: Store Data 52 Step 7: Visualize Data 53 What This Chapter Covered 53 Key Questions for Reflection 54 Exercise 54 Chapter 4 Data Modeling 55 OLTP vs. OLAP 57 Oltp 57 Olap 57 Modeling for OLTP 58 How to Create an ER Diagram 58 Understanding Cardinality 60 One-to-One (1:1) 60 One-to-Many (1:N) 61 Many-to-Many (N:M) 61 Building an ER Diagram for a Growth Use Case 63 Step 1: Identify Your Entities 63 Step 2: Define the Relationships 63 Step 3: Add Attributes 64 Step 4: Diagram It Out 65 Step 5: Think Through Growth Questions 65 Step 6: Avoid Modeling Pitfalls 66 Step 7: Get Ready for the Next Layer 67 Normalization 67 What Is a Relation? 68 Keys: Primary, Foreign, and Composite 69 Functional Dependencies 70 Normalization 71 Modeling for OLAP 76 Facts and Dimensions 76 Denormalization 78 Star and Snowflake Schemas 79 Star Schema 79 Snowflake Schema 79 Choosing Between Them 80 What This Chapter Covered 80 Key Questions for Reflection 81 Exercise 81 Chapter 5 What Are Experiments? 83 The Philosophy of Experimentation 84 Humility in Product Development 85 Experimentation as a Team Sport 85 Experimentation Protects Users 86 The Anatomy of an Experiment 86 Hypothesis Formation 87 Control and Treatment Groups 88 Randomization 89 Metrics and Scorecards 89 Duration and Sample Size 91 Why Experiments Matter in Growth Engineering 91 Common Misconceptions About Experimentation 93 "Experimentation Slows Us Down" 93 "Experiments Are Only for Small UI Tweaks" 94 "Only Data Scientists Should Run Experiments" 95 "We Can Just Measure After Launch Instead" 95 What This Chapter Covered 96 Key Questions for Reflection 97 Exercises 97 Chapter 6 Types of Product Experiments 99 Design Types 100 A/A Test 100 A/B Test 101 A/B/n Test 102 Multivariate Test 103 Holdout Groups 104 Switchback Test 105 Application Types 107 UI/UX Experiments 107 Onboarding Experiments 108 Notification Experiments 109 Pricing Experiments 109 Fake Door Experiments 110 Reverse Experiments 111 What This Chapter Covered 112 Key Questions for Reflection 113 Exercises 113 Chapter 7 Introduction to A/B Testing 115 What Makes a Fair Comparison 116 Triggering 117 Types of Triggering 118 Exposure-Based Triggering 118 Action-Based Triggering 118 Hybrid Triggering 119 Choosing the Right Trigger 119 Example: The Pro-Tip Onboarding Card 119 Randomization 120 Sample Ratio Mismatch 122 Statistical Significance 124 Power and Sample Size 126 Common Mistakes in A/B Testing 127 Stopping Too Soon 127 Running Overlapping Experiments 127 Ignoring Guardrail Metrics 128 Focusing on Significance over Impact 128 Skipping A/A Tests 128 Overlooking Novelty and Learning Effects 128 What This Chapter Covered 129 Key Questions for Reflection 130 Exercises 130 Chapter 8 Building a Growth Engineering Team 133 What Makes a Growth Engineering Team Unique 133 Team Composition and Roles 134 Growth Engineers 134 Product Managers 135 Data Scientists 135 Growth Designers 136 User Experience Researchers 136 Team Structure 137 Centralized Model 137 Embedded Model 138 Hybrid Model 138 Cultural Foundations 139 Experiment over Opinion 139 Shared Metrics and Transparency 140 Learning Loops and Post-Mortems 140 Building Trust for Growth 141 Hiring and Upskilling for Growth 141 The Growth Engineer''s Career Path 143 The Cadence of Growth Teams 144 Weekly Growth Review 144 Hypothesis Review 144 Scorecard Syncs 145 Sharing Learnings 145 What This Chapter Covered 145 Key Questions for Reflection 146 Exercises 146 Chapter 9 The Future of Growth Engineering 149 AI and the Future of Experimentation 150 Designing Experiments 151 AI-Assisted Development 151 Autonomous Experiment Execution 152 AI-Assisted Analysis and Insight Generation 153 How AI Changes the Role of the Growth Engineering Team 155 Growth Engineer 155 Product Manager 156 Data Scientist 158 Designers and UX Researchers 160 Ethics, Privacy, and Responsible Growth in an AI-Driven Era 161 What This Chapter Covered 163 Key Questions for Reflection 164 Exercises 164 Chapter 10 The Growth Engineer''s Workflow 165 Standup 166 Product Alignment 167 Engineering Design 168 Implementation 169 Bug Bash 171 Rollout 172 Scorecard Review 173 Retrospective 174 Communicating Impact 175 What This Chapter Covered 177 Key Questions for Reflection 177 Exercises 178 Index 179.