How LinkedIn interviews Data Analysts

LinkedIn publishes an outline of its hiring process rather than a role-specific script. Screen, take-home, case, behavioral and onsite are preparation categories on this page, not LinkedIn's round names.

• The recruiting call. If you meet the qualifications and are selected to move forward, the recruiting team schedules a call, and may also schedule a video call with the hiring manager or another team member.
• The interview phase. You meet several more team members, and each interview focuses on a different skill area. For an analyst, expect SQL, metrics, communication and judgment to be natural divisions, though LinkedIn does not list them.
• Case study or whiteboard. Depending on the role, candidates may be asked to present a case study or do a whiteboard exercise. An analyst is a plausible candidate for the case study form.
• The decision. LinkedIn may need several weeks to reach a decision after the interviews.

LinkedIn does not publish the number or length of interviews for a Data Analyst.

One skill area per interview is the published design. For an analyst that means each conversation can go all the way down: a SQL interview can keep adding constraints, a metrics interview can keep asking what would make the number wrong. Prepare each area for sustained questioning.

Data Analyst interviews in general assess:

• SQL and data cleaning. Joins, aggregations, window functions, and catching duplicates and nulls before reporting.
• Metrics and dashboards. Defining a metric so two people compute the same value, and knowing when it misleads.
• Communicating findings. Turning a result into a decision; a case study presentation, if asked for, is this skill in its purest form.
• Business judgment. Choosing which analysis matters most and defending the choice.

LinkedIn's published outline is the same for every role; the differences below are general practice.

• Compared with Data Scientist interviews, expect less modeling and experiment design and more metric definition, descriptive analysis and clear recommendations.
• Compared with Data Engineer interviews, expect little on pipeline architecture and more on interpreting data that already exists, including its flaws.
• Compared with other companies, the published features to prepare for are one skill area per interview, a possible case study presentation, and a decision that may take several weeks.

LinkedIn says it may need several weeks to reach a decision after the interviews and publishes nothing else about timing.

The difficulty is depth. A single-topic interview leaves nowhere to hide a thin area, so an analyst whose SQL is strong but whose metric definitions are vague will be found out in the metrics conversation. If a case study is requested, the presentation itself becomes a test of communication, and the questions afterward test whether the analysis holds up.

Mirror the published structure: one area at a time, to depth.

• SQL under sustained questioning. Practice problems where the interviewer keeps adding constraints, and narrate your checks.
• Metric definitions. For each metric on your resume, the definition, its weaknesses and a decision it changed.
• A case study you can present. Build one from a past analysis, with charts you can defend and a recommendation at the end, since a case study presentation is a published possibility.
• Judgment cases. Practice choosing among several analyses and explaining the choice in terms of member or business impact.
• Rehearse the format. A mock interview with SubcueAI lets you practice one skill area under sustained follow-up questions.