Amazon Business Intelligence Interview Guide

What strikes us most reviewing Amazon BIE candidate experiences is how consistently the process splits into two distinct tests — and how often candidates underestimate the second one. The SQL bar is real and well-documented: we've seen window functions, LAG() for period-over-period calculations, Nth-highest salary problems, NULL handling across join types, and time-based aggregations come up repeatedly across nearly every loop. But the candidates who got offers didn't just write correct SQL — they narrated their reasoning out loud, connected queries to business context, and stayed composed when interviewers pushed back or added constraints mid-question.

The Leadership Principles component is where we've seen the most unexpected rejections. Multiple candidates reported that LP questions weren't a warm-up — they were load-bearing. The Bar Raiser round, which appears consistently across experiences, is entirely behavioral and conducted by someone outside the hiring team specifically to apply a higher standard. One successful candidate described replacing 'we' with 'I' throughout every STAR story as a deliberate strategy, and another quantified a dashboard deprecation decision down to a 40% load time improvement. That level of specificity isn't polish — it's what separates passing answers from forgettable ones. Candidates who treated LP prep as secondary to SQL prep tended not to advance.

There's also a third element that catches people off guard: data visualization and dashboard design judgment. Several candidates encountered prompts asking them to critique an existing chart, defend axis choices, or design a dashboard around a business metric drop — including defining a North Star metric and guardrail metrics. This isn't standard in most BI interviews, and it's clearly testing whether you think like a business analyst, not just a query writer. If you're preparing for this role, that design-thinking layer deserves dedicated attention alongside your SQL practice.