How Meta interviews Data Analysts

Meta does not publish a Data Analyst specific process. Its published loop is for software engineers, and it is the only documented reference, so it is described here with that label.

• Technical screen (software engineers). A 45-minute conversation with a Meta engineer, primarily coding: 5 minutes of introductions, 35 minutes of coding, 5 minutes for the candidate's questions, in a collaborative editing tool when remote and on a whiteboard in person.
• The full loop (software engineers). A coding interview, a design interview, and a behavioral interview covering previous work experience and motivations, each of the design and behavioral interviews 45 minutes long.

For an analyst, the published loop tells you the style of Meta's interviews: time-boxed, single-topic and with a dedicated behavioral conversation. It does not tell you the number of rounds, whether there is a take-home, or whether coding means SQL. Screen, take-home, case, behavioral and onsite are preparation categories on this page, not Meta's round names.

The published loop separates technical execution, design and previous work into distinct interviews. An analyst should prepare for the same separation, filled with analyst content, while knowing this is inference from the engineering loop.

Data Analyst interviews in general assess:

• SQL and data cleaning. Joins, aggregations and window functions written quickly and correctly, with the duplicates and nulls found before the answer is reported.
• Metrics and dashboards. Defining a metric so two people compute the same number, and knowing when it misleads.
• Communicating findings. Turning a result into a decision, which is what a behavioral interview about previous work will ask you to evidence.
• Business judgment. Choosing which analysis to run first and defending the choice.

Meta publishes nothing role-specific beyond software engineering, so 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 the published engineering loop, an analyst should expect coding to mean SQL rather than general programming, and the design conversation, if there is one, to be about a metric or an analysis plan rather than a system.

The published durations are per interview, 45 minutes, with 35 minutes of coding in the coding interviews for software engineers. Meta does not publish a timeline for any role.

If the analyst interviews follow the same time-boxing, the difficulty is density: a single technical problem worked without a break, then a separate conversation where previous work has to be described precisely. Analysts who practice SQL against a clock and who can summarize a past analysis in a minute are prepared for that; analysts who need time to warm up are not.

Mirror the published separation of technical, design and behavioral, filled with analyst material.

• Timed SQL. Practice problems in 35-minute blocks in a shared editor, narrating your checks.
• Metric stories. For each metric on your resume, the definition, its weaknesses and a decision it changed.
• Analysis plans. Practice turning a vague product question into a plan: the metric, the comparison, the confounders, the recommendation.
• Previous work and motivations. The published behavioral interview covers both; have specific incidents and an honest reason you want the role.
• Rehearse the format. A mock interview with SubcueAI helps you practice a single time-boxed technical problem followed by follow-up questions.