Senior Business Intelligence Developer Interview Questions: Your Guide to Landing the Job

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Toptal sourced essential questions that the best Business Intelligence developers and engineers can answer. Driven from our community, we encourage experts to submit questions and offer feedback.

What is a data cube (or “OLAP cube”)?

Before it is sent to a BI UI tool to be shown to the user, the BI data structure is described by a data cube. It is a multi-dimensional data representation made for better visualization, data slicing, and drill-down techniques. The user interface doesn’t show a real cube very often; instead, it shows 2D slices of it so it’s easier for people to read:

One denormalized fact table and several dimension tables that show the data cube’s dimensions make up a data cube. The star and snowflake schemas were specifically designed to aid in building data cube structures in memory.

An example schema might consist of:

• Time buckets—time dimension table
• Customers—customer dimension table
• Products—product dimension table
• Sales amount (units sold)—fact table

The data cube structure for this schema can be thought of like this: 2 .

Describe fact and dimension tables.

A fact table contains dimension keys and numerical values for some measures. Each dimension key represents a dimension that measures are for. Measures can be aggregated across dimensions to build a drillable data cube.

Dimension tables are dictionary tables used to display dimension labels and information on BI visual interfaces. 3 .

What are the steps to implement company BI analytics from the ground up?

• Build company analytical data storage (data warehouses, data marts).
• Come up with a way to store analytical data based on real company data and BI needs.
• First, add existing company data to analytical data storage. Then, make sure it’s always up to date.
• Set up BI tools on top of analytical data storage.
• Develop BI reports.
• Maintain and modify BI reports according to changing needs.

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Name some benefits of data normalization.

The candidate should name at least two benefits from those listed below. It can be in their own words, as long as it’s close in meaning. The more benefits they can name, the better.

Data normalization:

• Removes data duplication.
• Allows finer transaction granularity. The data in each referenced table could be changed in its own transaction, without affecting the relationships between the tables that use foreign keys.
• Enables clearer referential integrity. Once normalization is done, business objects and their relationships can be modeled in a way that is as close to real life as possible.
• Allows incremental schema changes. Changing the structure of tables that are referenced doesn’t happen when you add or remove columns from one table.

5 .

When should you use a data mart instead of a single data warehouse? What is a data mart?

A data mart is a place where some of a company’s data is kept that is specific to a department, type of activity, or set of subproblems.

By putting data into separate “data marts,” you can improve performance and give BI analysts and business users different tasks to do.

This strategy is a matter of design and operational convenience. Although there is no clear-cut answer to the question of when to use a data mart, most people agree that it’s a good idea when a company has different business lines that have very different data and reporting needs.

Suppose the same company builds trucks and runs an online game app. It would probably be best to keep these sub-issues separate in a data mart. 6 .

What are the star and snowflake schemas?

The star schema consists of dimension and fact tables. Each dimension table represents a “metric” that can be used in BI reporting. A fact table references dimension tables for each corresponding metric the fact table covers.

The snowflake schema builds on the star schema by letting dimension tables be further normalized and split into main and secondary dictionary tables. 7 .

Define OLTP and OLAP. What is the difference? What are their purposes?

OLTP stands for “online transactional processing. ” It is used for company business applications. They are most often customer- (i. e. , people- or business-) facing.

OLAP stands for “online analytical processing. It’s used by department heads and top management to look at the inside of a company and figure out how to run it. 8 .

Which BI tools have you used, and what are their good and bad sides?

There are numerous BI tools on the market, but among the best-known are:

• Oracle Business Intelligence Enterprise Edition (OBIEE)
• IBM Cognos Analytics
• MicroStrategy
• The SAS product line
• SAP BusinessObjects
• Tableau
• Microsoft Power BI
• Oracle Hyperion
• QlikView

This type of free-form question isn’t about the candidate providing a correct answer, per se. It’s more about starting a conversation so that interviewers can find out how knowledgeable the candidate really is and how that knowledge fits in with what the company needs right now. 9 .

What is the purpose of BI?

BI provides quick and simple methods to visualize company metrics, generate reports, and analyze data.

These methods, in turn, help top management to:

• Analyze existing trends.
• Lay out company development plans.
• Ensure such plans are executed as scheduled.
• Detect anomalies and problems.
• Apply corrective actions.

10 .

Name some benefits of data denormalization.

The candidate should name at least two benefits from those listed below. It can be in their own words, as long as it’s close in meaning. The more benefits they can name, the better.

Data denormalization provides:

• Simpler initial data schema design.
• Better data write/read performance.
• Direct applicability in data warehouses. In data warehouses, fact and dimension tables are often made without data normalization in mind so that data can be retrieved quickly and easily.
• Better pre-compute and query performance for slice-and-dice and drill-down analysis in data cube BI

11 .

What are the primary responsibilities of a BI developer?

BI developers are generally expected to:

• Analyze company business processes and data.
• Standardize company data terminology.
• Gather reporting requirements.
• Match the above requirements against existing data.
• Build BI reports.
• Analyze the fleet of existing reports for further standardization purposes.

This question can be helpful as an opening one—not only to weed out bad candidates and put qualified ones at ease, but also to talk about any unusual duties that might come with the job.

There is more to interviewing than tricky technical questions, so these are intended merely as a guide. Not every good candidate for the job will be able to answer all of them, and answering all of them doesn’t mean they are a good candidate. At the end of the day, hiring remains an art, a science — and a lot of work.

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