This article lists 30 important interview questions for Business Intelligence Engineering roles, focusing on skills relevant in the AI era. It aims to help both candidates and interviewers navigate the evolving landscape of data and analytics.

• The article compiles 30 BI engineering interview questions specifically framed around how AI is reshaping the role, not just traditional BI skills.
• It pushes beyond tool-specific knowledge (SQL, Tableau, Power BI) to test how candidates reason through data governance, integrity, and security issues.
• It frames machine learning familiarity as now a baseline expectation for BI engineers, not a separate specialty.
• It stresses evaluating soft skills like translating technical findings into business insights for cross-functional teams as equally important as technical chops.

Since the inception of SQL in 1974, there has been a recurring dream to replace data analytics developers with tools that simplify the querying process. Each decade has seen innovations that aim to democratize data access, yet the complex intellectual work of understanding business needs and making informed decisions remains essential. Advances like AI can enhance efficiency but do not eliminate the crucial human expertise required in data analytics.

• SQL was designed in 1974 by IBM's Chamberlin and Boyce specifically to let non-programmers query data in plain English, yet this self-service dream has resurfaced every decade (OLAP in the 80s, semantic layers now) without ever eliminating the need for developers.
• AI can now generate SQL and build analytical models faster than ever, but it automates only the mechanical coding, not the judgment calls about which metrics or definitions actually matter.
• The real bottleneck was never syntax or tooling but the intellectual work of understanding business context — a gap AI accelerates around but doesn't close.