Preparing for an interview at Meta Platforms requires a structured approach that balances technical practice with strategic thinking. You cannot rely solely on your coding skills; you must also demonstrate strong business acumen and a structured communication style. The hiring team evaluates candidates across several core dimensions to ensure they can thrive in a highly autonomous environment.

Technical Execution – Your ability to write clean, efficient, and accurate queries or scripts is fundamental. Interviewers look for structured logical flow, proper syntax, and a deep understanding of data manipulation techniques.

Product Sense – This criterion measures your ability to translate vague product questions into structured analytical frameworks. You must show that you understand user behavior, product goals, and how to measure success quantitatively.

Structured Problem-Solving – When faced with ambiguous scenarios, you must be able to break down the problem into logical components. Interviewers assess how systematically you approach data investigations and whether you can identify edge cases.

Communication & Influence – You must be able to articulate your thoughts clearly, explain the "why" behind your technical choices, and demonstrate how you use data to influence cross-functional stakeholders.

The interview process at Meta Platforms is highly structured, transparent, and designed to evaluate both your technical capabilities and your alignment with company culture. Candidates typically describe the process as rigorous but incredibly smooth, with supportive recruiters who provide detailed prep materials and timely feedback at every stage. The timeline is designed to move efficiently, but you should expect high standards of evaluation throughout.

The process begins with an initial recruiter screen, which focuses on your background, career goals, and basic alignment with the role. If you pass this stage, you will move on to a technical screening round, which typically consists of a live coding assessment and a discussion of your past experiences. Candidates who successfully navigate the screen are invited to the virtual onsite loop, which involves deep dives into product sense, technical execution, and behavioral scenarios.

The visual timeline above outlines the standard progression of the hiring pipeline, highlighting the key transition points from your initial application to the final decision. It is important to treat each stage as an opportunity to showcase different aspects of your analytical toolkit. Managing your preparation pace around these distinct phases will help you stay focused and perform at your best.

To excel in the Meta Platforms interview loop, you must understand exactly what is being tested in each core evaluation area. The expectations are high, and understanding the nuances of these sessions will help you stand out.

Technical & Coding Execution

The technical screen is highly focused on execution speed, accuracy, and code quality. You will typically face a half-hour live coding assessment where you must solve data manipulation problems in real time. Interviewers want to see how you translate business requirements into clean code.

Be ready to go over:

• SQL Window Functions – Mastering partitions, lead/lag, and ranking functions is critical for user behavior analysis.
• Data Aggregations and Joins – Understanding complex joins, handling null values, and writing efficient subqueries or CTEs.
• Performance Optimization – Explaining how to minimize computational complexity when working with massive datasets.
• Advanced concepts (less common) – Recursive queries, custom user-defined functions, and advanced array manipulation.

Example scenarios:

• "Write a query to calculate the rolling 30-day active user count for a specific platform feature."
• "Identify users who logged in on consecutive days using a single optimized SQL query."
• "Resolve a data discrepancy where the sum of country-level metrics does not match the global metric total."

Product Sense & Metric Definition

This interview evaluates your ability to think like a product owner. You will be asked to define metrics, evaluate product changes, and diagnose unexpected shifts in user behavior. The key to success here is using a structured framework rather than listing random metrics.

Be ready to go over:

• Metric Frameworks – Defining North Star, driver, and guardrail metrics for different product surfaces.
• A/B Testing & Experimentation – Understanding sample sizes, statistical significance, and interpreting mixed experiment results.
• Root Cause Analysis – Systematically diagnosing metric anomalies (e.g., why a specific metric dropped suddenly).
• Advanced concepts (less common) – Network effects in social graphs, long-term user cohorts, and ecosystem trade-offs.

Example scenarios:

• "How would you design an experiment to test a new ranking algorithm for the Instagram feed?"
• "Define the success metrics for the launch of a new peer-to-peer payment feature inside WhatsApp."
• "Explain how you would investigate a sudden 10% drop in video views on Facebook."

Experience & Behavioral Alignment

At Meta Platforms, past work experience is highly prioritized. Interviewers want to hear about real impact, how you deal with cross-functional friction, and how you drive projects forward in the face of ambiguity.

Be ready to go over:

• Data-Driven Influence – How you used data to persuade a product manager or engineering lead to change course.
• Handling Ambiguity – Structuring a project when the business goal or data source was poorly defined.
• Prioritization and Execution – How you manage competing requests from multiple stakeholders.
• Advanced concepts (less common) – Navigating organizational restructuring and managing data quality crises.

Example scenarios:

• "Tell me about a time you identified a product opportunity that was completely overlooked by the team."
• "Describe a situation where a cross-functional partner insisted on launching a feature despite negative data indicators."
• "Walk me through a project where you had to build a data model from scratch with highly unstructured data."