Introduction to Gathering Business Requirements

In the fast-paced world of big tech, effective data modeling is vital for building scalable, efficient systems that drive business value. As a mid-level or senior data engineer interviewing at FAANG companies, your ability to gather and interpret business requirements for data modeling is a critical skill. This guide will help you navigate the requirements gathering process in a data modeling interview.

Relevance in data modeling interviews

In data modeling rounds of FAANG interviews, your approach to gathering and interpreting business requirements sets the stage for your entire solution. Interviewers are evaluating:

1. Your ability to grasp the core business problem
2. Your skill in translating business needs into technical requirements
3. Your capacity to think critically about scalability and performance
4. Your communication skills and ability to ask insightful questions

Requirement gathering checklist

Use this checklist to systematically gather requirements during interviews:

Functional Requirements:

1. Business Objectives:
   • What is the primary goal of the data model?
   • Who are the key stakeholders?
2. Key Metrics:
   • What metrics are critical for business decisions?
   • What data points are needed for these metrics?
3. Use Cases:
   • What are the primary use cases for the data model?
   • Are there specific query patterns?

Non-functional Requirements:

1. Performance:
   • What are the latency requirements for different queries?
   • How frequently will the data be accessed?
2. Data Volume:
   • What is the current volume of data?
   • What is the expected growth rate?
3. Retention Policies:
   • How long does the data need to be retained?
   • Are there legal or compliance requirements?

We'll explore each of these areas in detail throughout this guide.

Applying this in interviews

When faced with a data modeling question in a FAANG interview, use these steps to gather comprehensive requirements:

1. Clarify the scope and primary business objective
2. Identify 3-5 key metrics for each relevant category (operational, financial, user engagement, performance)
3. Determine the most common query patterns and their characteristics
4. Classify latency requirements for main features into real-time, near real-time, or batch
5. Establish data volume estimates and growth projections for main entities
6. Determine data retention needs for different types of data

The next few lessons will break each step down in detail.