Recruiter's Glossary

Examples: Dimensional Modeling • Time Series Analysis • Master Data Management

Data Warehouse

Term from Data Analytics industry explained for recruiters

A Data Warehouse is like a company's digital storage facility that holds all its important business information in one organized place. Think of it as a central library where all company data from different sources (like sales, customer information, and operations) is collected, organized, and made easy to access. Companies use Data Warehouses to help them make better business decisions by looking at their historical data all in one place. It's different from regular databases because it's specifically designed to analyze large amounts of historical data rather than handle day-to-day transactions. When you see this term in resumes, it usually means the person has experience with organizing and managing large amounts of company data for business analysis purposes.

Examples in Resumes

Designed and implemented a Data Warehouse solution that reduced reporting time by 60%

Maintained and optimized Data Warehouse infrastructure supporting 500+ business users

Led migration of legacy systems to modern Data Warehouse architecture

Developed Data Warehouse solutions to integrate multiple data sources for business intelligence

Typical job title: "Data Warehouse Engineers"

Also try searching for:

Data Engineer Business Intelligence Developer Data Warehouse Developer Data Warehouse Architect BI Engineer Data Infrastructure Engineer ETL Developer

Where to Find Data Warehouse Engineers

Online Communities

Job Boards

Professional Networks

Example Interview Questions

Senior Level Questions

Q: How would you approach designing a data warehouse from scratch for a large retail company?

Expected Answer: A strong answer should mention gathering business requirements, planning data sources integration, choosing appropriate architecture, considering scalability, and implementing data quality measures. They should emphasize understanding business needs before technical implementation.

Q: How do you handle slowly changing dimensions in a data warehouse?

Expected Answer: Should explain in simple terms how they track historical changes in business data, like when customer information changes over time, and methods to maintain accurate historical reporting.

Mid Level Questions

Q: What strategies do you use to improve data warehouse performance?

Expected Answer: Should discuss practical approaches like organizing data effectively, setting up proper data refresh schedules, and using techniques to make data retrieval faster for business users.

Q: How do you ensure data quality in a data warehouse?

Expected Answer: Should explain methods for checking data accuracy, completeness, and consistency, including regular data validation and cleaning processes.

Junior Level Questions

Q: What is the difference between a data warehouse and a regular database?

Expected Answer: Should explain that a data warehouse is designed for business analysis and reporting, while regular databases handle day-to-day transactions and operations.

Q: Can you explain what ETL means in data warehousing?

Expected Answer: Should describe ETL as the process of getting data from various sources (Extract), cleaning and organizing it (Transform), and putting it into the data warehouse (Load) in a business-friendly format.