You're interviewing for a data warehousing job in 2026. What will you need to know?

1 Data warehouse architecture

You'll need to demonstrate your understanding of the different types of data warehouse architectures, such as centralized, distributed, hybrid, or cloud-based, and their advantages and disadvantages. You'll also need to explain how you would choose the appropriate architecture for a given scenario, based on factors such as data volume, variety, velocity, veracity, and value. You might be asked to compare and contrast different data warehouse models, such as the star schema, the snowflake schema, or the data vault model, and how they affect the performance, scalability, and flexibility of the data warehouse.

2 Data warehouse design

You'll need to show your skills in data warehouse design, such as how to define the business requirements, scope, and objectives of the data warehouse project, how to identify the data sources and data quality issues, how to design the data extraction, transformation, and loading (ETL) processes, and how to implement the data warehouse schema and data marts. You'll also need to demonstrate your knowledge of the best practices and principles of data warehouse design, such as data normalization, data denormalization, data partitioning, data compression, data encryption, and data governance.

3 Data warehouse tools

You'll need to be familiar with the various tools and technologies that are used to build and manage data warehouses, such as data warehouse platforms, ETL tools, data integration tools, data quality tools, data modeling tools, data analysis tools, and data visualization tools. You'll also need to keep up with the latest trends and innovations in data warehouse tools, such as cloud-native data warehouses, real-time data warehouses, self-service data warehouses, or machine learning-enabled data warehouses. You might be asked to demonstrate your proficiency in using one or more of these tools, or to compare and contrast their features and functions.

4 Data warehouse testing

You'll need to prove your ability to test and validate the data warehouse, such as how to plan and execute the test cases, how to use different types of testing methods, such as unit testing, integration testing, system testing, or user acceptance testing, and how to measure and report the test results and quality metrics. You'll also need to be aware of the common challenges and pitfalls of data warehouse testing, such as data inconsistency, data duplication, data corruption, or data security breaches, and how to avoid or resolve them.