One of the toughest questions I was asked wasn’t about SQL or machine learning, it was a business case:

“Our company’s revenue dropped by 15% last month. You have no additional information. How would you investigate the problem?”

What made it challenging was that there wasn’t a single correct answer. The interviewer wanted to evaluate how I structured the problem, identified potential hypotheses, and prioritized my analysis rather than jumping straight into technical solutions.

I started by breaking the problem into key dimensions: product, geography, customer segments, acquisition channels, pricing, seasonality, and operational changes. From there, I explained what data I would request, the KPIs I’d examine, and how I’d validate each hypothesis before drawing conclusions.

Looking back, I realized that data interviews often assess your ability to think like a business analyst as much as your technical skills. Knowing SQL or Python is important, but being able to communicate a logical, data-driven approach to solving ambiguous problems is what really stands out.