Meta Data Analyst candidates report recruiter screening followed by timed SQL/Python work and interviews spanning product analytics, statistics, and behavioral communication. Prepare to reason aloud under a tight clock.
$168K
Avg. Base Comp
$229K
Avg. Total Comp
4-6 rounds
Typical Rounds
3-6 weeks
Process Length
Meta Data Analyst candidates commonly describe a process that tests more than query writing. Timed SQL and Python execution is a recurring feature: reports include screens with five questions in each language, six mixed questions in an hour, or shorter SQL and Python segments. The technical work is often practical—joins, aggregations, CTEs, window functions, conditional metrics, and multi-table transaction-style data—so accuracy on assumptions and edge cases matters alongside speed.
Product and analytics discussions recur across reports. Candidates have been asked to frame A/B tests, choose metrics for a product decision, or reason about engagement and social-connection scenarios. Explain the hypothesis, comparison, metric definition, and decision logic in a clear order; interviewers may follow up on assumptions rather than accept a one-line answer. Some candidates also report separate statistics or probability conversations.
Behavioral preparation should stay concise. Reported prompts include conflict, prioritization, team rapport, stakeholder communication, and motivation for Meta. Several candidates describe virtual loops with distinct technical and behavioral conversations, while the exact sequence differs by team and candidate. Thin evidence does not establish one universal format. Practice narrating SQL decisions and product reasoning aloud, then keep a small set of adaptable examples ready for behavioral follow-ups.
Synthesized from 15 candidate reports by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Meta process.
The hardest part for me was not that the questions were unusually difficult; it was having to think aloud continuously while moving quickly. After submitting my resume and cover letter, I went through four interviews: a behavioral round, a coding round, a SQL round, and a product analytics round. The interviewers seemed tired, so I made a point of clearly narrating my reasoning rather than waiting for much back-and-forth.
The behavioral interview included a conflict question, specifically asking about a time I had conflict with other people. The SQL portion focused on writing queries, including a medium LeetCode-style aggregation problem. The product analytics discussion covered A/B test design. The coding portion was fast-paced: I faced several LeetCode-style questions and needed to solve them quickly. Overall, the individual questions did not feel extremely hard, but the pace and expectation to keep communicating made the process demanding.
I did not receive an offer. My main advice is to practice explaining your approach out loud throughout each answer, and prepare both SQL aggregation problems and the reasoning behind an A/B test design. For coding, get comfortable solving several questions under time pressure rather than relying on a single deep-dive problem.
Prep tip from this candidate
Practice narrating your reasoning continuously while solving fast-paced coding questions. Drill medium SQL aggregation queries and be ready to explain an A/B test design, not just write SQL.
Share your own interview experience to unlock all reports, or subscribe for full access.
Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Meta
Write a query that returns all neighborhoods that have 0 users.
| Question | |
|---|---|
| 2nd Highest Salary | |
| Comments Histogram | |
| Employee Salaries | |
| Experiment Validity | |
| 500 Cards | |
| Session Difference | |
| Last Transaction | |
| Like Tracker | |
| Random SQL Sample | |
| Subscription Overlap | |
| Weighted Keys | |
| Button AB Test | |
| Average Order Value | |
| Largest Salary by Department | |
| Top 3 Users | |
| Swipe Precision | |
| Notification Deliveries | |
| Project Budget Error | |
| Decreasing Comments | |
| Longest Streak Users | |
| Impression Reach | |
| Bank Fraud Model | |
| Lazy Raters | |
| Closed Accounts | |
| Identifying User Sessions | |
| Liked Pages | |
| Network Experiment Design | |
| Booking Regression | |
| Reducing Error Margin |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an opening recruiter email, call, or hiring-manager conversation covering their experience, education, motivation, and sometimes why Meta. This stage may also include brief SQL or Python questions, so a concise background walkthrough and basic technical readiness can both matter.
Several candidates report a timed screen combining SQL and Python, including formats of five questions in each area or six mixed questions in an hour. Reported SQL tasks use joins, CTEs, aggregations, conditional percentages, and transaction-style tables; Python may test practical data structures and validation logic.
Candidates report product-sense discussions about feature or engagement decisions, often with follow-up questions about metrics, comparisons, or A/B-test design. Some also encountered statistics or probability. Typically, explain your assumptions, define the outcome measure, and walk through the reasoning rather than jumping to a conclusion.
Reported loops vary, but candidates describe separate conversations covering SQL, product sense or analytics, statistics, coding, data modeling, and behavioral questions. One-on-one sessions are often described as virtual, and several reports emphasize communicating continuously while working through time-constrained prompts.
Candidates report questions about conflict, task prioritization, team rapport, stakeholder communication, and past experience. One published report described four to six situation-based questions in a 45-minute session. Prepare focused examples that state the situation, action, and result without losing the key analytical detail.
