Growth Engineering - Rita Okonkwo

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Auteur(s) : Rita Okonkwo

Editeur : Wiley

Langue : Anglais

Parution : 01/04/2026

Format : Moyen, de 350g à 1kg

Nombre de pages : 208.0

ISBN : 9781394378463

Résumé :

Build software that users actually use with proven growth-oriented software development strategies

In Growth Engineering: How to Build Systems That Drive Product Success in an AI-Driven World, experienced software engineer with the Microsoft Experiences + Devices Growth team, Rita Okonkwo, delivers a strategic guide for anyone interested in building tech products that scale organically through smart technical choices.

You'll learn how clean architecture, thoughtful instrumentation, and experimentation frameworks directly influence growth outcomes. With a focus on practical systems and real-world decision-making, this book shows how to build software that gains traction, drives engagement, and supports continuous iteration.

You'll learn all about key growth engineering strategies like feature flighting, data-driven experimentation, logging, and metrics tracking. You'll find real-world case studies that break down design systems that support rapid iteration, and data-based product decision-making.

Inside the book:

• Why growth engineering matters and how engineers can get directly involved in it
• Experimentation strategies, including controlled rollouts and effective A/B testing techniques
• How to build scalable data pipelines and integrate real-time analytics
• Ways to create a growth-first engineering culture, generating faster iterations without sacrificing quality

Perfect for software engineers, product managers, and developers interested in building products that users love, Growth Engineering: How to Build Systems That Drive Product Success in an AI-Driven World is a must-read for entrepreneurs, founders, and other technology business leaders ready to discover how to consistently create commercially successful software....

Biographie:

Preface xv
Foreword xvii
Introduction xxi

Chapter 1 Growth Engineering 1

Chapter 2 Observability 7

Chapter 3 Data Pipelines 29

Chapter 4 Data Modeling 55

Chapter 5 What Are Experiments? 83

Chapter 6 Types of Product Experiments 99

Chapter 7 Introduction to A/B Testing 115

Chapter 8 Building a Growth Engineering Team 133

Chapter 9 The Future of Growth Engineering 149

Chapter 10 The Growth Engineer's Workflow 165

Key Questions for Reflection 177
Exercises 178
Index 179

...

Sommaire:

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 Misconcept...