You’re looking to hire a data warehousing professional. How do you know if they have the right skills?

1 Data warehousing basics

The first thing you need to check is whether the candidate understands the basic concepts and principles of data warehousing. Data warehousing is the process of designing, building, and maintaining a centralized repository of data from various sources, such as transactional systems, operational databases, external sources, and so on. The data warehouse is organized into subject areas, such as customers, products, sales, and so on, and follows a dimensional model, which consists of facts and dimensions. Facts are numerical measures of business events, such as sales amount, order quantity, or profit margin. Dimensions are descriptive attributes that provide context to the facts, such as date, time, location, customer, or product. The candidate should be able to explain these concepts and how they relate to each other in a data warehouse.

2 Data warehousing tools and technologies

The second thing you need to check is whether the candidate has experience and proficiency with the tools and technologies that are used to implement and manage a data warehouse. Depending on your specific needs and preferences, you may use different platforms, such as cloud-based services, on-premise servers, or hybrid solutions. You may also use different software, such as ETL (extract, transform, and load) tools, data modeling tools, data quality tools, data governance tools, and so on. The candidate should be able to demonstrate their knowledge and skills with the tools and technologies that are relevant to your data warehousing project. They should also be able to compare and contrast the advantages and disadvantages of different options and make informed recommendations.

3 Data warehousing best practices and standards

The third thing you need to check is whether the candidate follows the best practices and standards that are established for data warehousing. Data warehousing is not a one-time project, but a continuous process that requires planning, design, development, testing, deployment, maintenance, and improvement. The candidate should be aware of the best practices and standards that are applicable to each stage of the data warehousing lifecycle. For example, they should know how to conduct a business requirements analysis, how to design a logical and physical data model, how to implement data quality checks and validations, how to document and communicate the data warehouse architecture and metadata, how to optimize the data warehouse performance and scalability, and so on.

4 Data warehousing challenges and solutions

The fourth thing you need to check is whether the candidate can identify and solve the common challenges and problems that arise in data warehousing. Data warehousing is not a simple or straightforward task, but a complex and dynamic one that involves many technical and business issues. The candidate should be able to recognize and address the challenges and problems that they may encounter in data warehousing, such as data integration, data quality, data security, data consistency, data availability, data accessibility, data usability, and so on. They should also be able to propose and implement solutions that are feasible, effective, and efficient.

5 Data warehousing trends and innovations

The fifth thing you need to check is whether the candidate is up to date and curious about the latest trends and innovations in data warehousing. Data warehousing is not a static or stagnant field, but a evolving and advancing one that is influenced by the changes and developments in the data landscape. The candidate should be aware of and interested in the new and emerging trends and innovations in data warehousing, such as data lake, data virtualization, data federation, data warehouse automation, data warehouse as a service, and so on. They should also be able to evaluate and adopt the trends and innovations that are suitable and beneficial for your data warehousing project.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?