Analytics engineer behavioral: vague requests, stakeholder conflict, projects in trouble

In two behavioral rounds for an analytics engineering role, one with a senior member of the team and one with a principal product manager, the interviewers spent little time on coding. They probed how the candidate works with business partners: handling ambiguous requirements, working with data science, product and engineering, resolving disagreements and conflicting requests, prioritizing several projects, and dealing with a project in difficulty. The product manager's round focused on whether the candidate truly understands the business problem rather than only writing SQL and building pipelines. Answer each part with a specific example from your own work. ### Clarifying Questions - Should the examples come from analytics engineering work (data models, pipelines, metric definitions), or can they come from analysis projects? - Is one example per part expected, or may one project cover several parts? - In the product manager's round, how much of each answer should cover the business decision compared with the data work? ### Part 1 — Turn an ambiguous request into a data problem Tell me about a time a stakeholder brought you a vague or ambiguous request. How did you turn the business question into a data problem, and how did you judge whether the work had business impact? ```hint Start from the decision Ask what the requester would do differently depending on the answer. That decision tells you which metric, which grain and which deadline matter. ``` #### What This Part Should Cover - How the real question behind the request was uncovered, and whether the requirement was challenged - How the request became definitions, scope and a first deliverable - Evidence that the work changed a decision, and how that impact was judged ### Part 2 — Resolve a disagreement or conflicting requests Tell me about a time you disagreed with a partner in data science, product or engineering, or two stakeholders asked you for conflicting things. How did you resolve it, and how did you prioritize your work across competing projects? ```hint Positions versus goals Conflicting asks usually serve different goals. Think about what each person actually needed and who owned the final decision. ``` #### What This Part Should Cover - Understanding each side's goal and finding the shared objective - Using data and explicit criteria, rather than seniority or volume, to reach a decision and to prioritize - The outcome, and the working relationship afterwards ### Part 3 — Recover a project in difficulty Tell me about a project that ran into serious difficulty. What did you do, and how did you explain the underlying data problem to stakeholders without a technical background? ```hint Translate the impact A non-technical audience needs to know which numbers are affected, by how much, and what to do now, not how the pipeline works. ``` #### What This Part Should Cover - Diagnosing the root cause, and the actions taken, including trade-offs such as moving a deadline - Communicating the problem early and in business terms - What changed afterwards to prevent a repeat ### What a Strong Answer Covers - Specific stories told in the first person, separating the candidate's own actions from the team's - Business understanding: the decision behind each request, and a willingness to challenge requirements and propose alternatives - Influence without authority across data science, product and engineering - Measurable results or concrete decisions, and honest reflection on what would be done differently - Clear, jargon-free communication suited to the audience ### Follow-up Questions - The stakeholder rejected your reframing and insisted on the original request. What would you have done? - How did you decide what not to build, and how did you tell the requester? - How did you know the model or dashboard you delivered was actually used? - Looking back at the project in difficulty, which early warning sign did you miss?