BI Developer Interviews: SQL, Data Modelling, and Dashboards That People Actually Use

Business intelligence developer interviews look simple from the outside: write some SQL, talk about a dashboard, done. In practice, strong BI loops test three things at once: whether you can write correct, efficient queries; whether you understand how to model data so that reporting stays fast and consistent; and whether you can turn a vague stakeholder request into a report that drives decisions. Here is how to prepare for each. SQL rounds: correctness first, then performance Expect live SQL questions on joins, aggregations, window functions, and date handling. Classic prompts include "Find the top three products by revenue in each region," "Calculate a 7-day rolling average of daily active users," and "Identify customers whose spend dropped more than 30 percent month over month." Talk through your approach before typing, state assumptions about nulls and duplicates, and test your logic on a tiny example. Once the query is correct, be ready to discuss indexing, partitioning, and why a CTE or temporary table might help readability or performance. Data modelling: stars, grain, and slowly changing dimensions Most BI interviews include a modelling discussion. Know the difference between fact and dimension tables, why a star schema is usually preferred for reporting, and what "grain" means. If the interviewer asks you to model sales data, start by defining the grain explicitly, for example one row per order line, before choosing dimensions such as date, customer, product, and store. Be ready to explain slowly changing dimensions, especially Type 1 versus Type 2, and when each makes sense. Tools: Power BI, Tableau, Looker, and the semantic layer You will likely be asked about the tools you know. Rather than listing features, explain how you used them. In Power BI, talk about DAX measures, relationships, and row-level security. In Tableau, talk about calculated fields, LOD expressions, and extract versus live connections. In Looker or dbt-based stacks, discuss the semantic layer and how you kept metric definitions consistent across teams. Consistency of definitions is one of the most valuable things a BI developer delivers, and interviewers know it. The stakeholder case A common case question is "The sales director says the dashboard numbers are wrong. What do you do?" Strong answers start with clarifying which number, compared to what source, and over what period. Then they move through data freshness, filters, definitions, and pipeline issues before touching the dashboard itself. Show that you stay calm, communicate clearly, and document the fix. Portfolio and examples If you can, bring one or two dashboards you are proud of, with sensitive data removed, and explain who used them, what decision they supported, and what you would change now. Talking about adoption, not just visuals, signals maturity. Practicing for the live parts Live SQL under time pressure is where many good candidates stumble. Timed practice on realistic datasets, plus mock interviews with a friend, helps a lot. Some candidates also use Linkjob AI (https://linkjob.ai), which gives real-time help during live job interviews and technical interviews, to keep their reasoning structured when a query or modelling question gets complicated. Whatever you use, aim to think out loud clearly so the interviewer can follow your logic. Final thought BI teams hire people who make data trustworthy and easy to use. If your answers show careful SQL, clean modelling, and empathy for the people reading the dashboards, you will be a strong candidate.