Meta Interview Questions

Meta Data Manipulation (SQL/Python) Interview Questions

Practice 1,159 real Meta interview questions for 2026. Covers top categories — Coding & Algorithms, Analytics & Experimentation, Data Manipulation (SQL/Python), Behavioral & Leadership, and System Design — across Software Engineer, Data Scientist, Machine Learning Engineer, Data Engineer, and Product Manager roles. Real questions from actual interviews with detailed solutions. Expect a software-engineering-heavy loop: timed algorithmic coding (trees, arrays, graph/maze problems, delimiter/CSV parsing), system-design prompts like leaderboards, flight search and online-judge architectures, and an increasingly common AI-assisted coding round that mirrors real workflows. Data Scientist rounds emphasize product analytics and experimentation—designing tests, diagnosing spend drops and bots, evaluating unconnected content, and writing SQL for multi-account, seller, and vehicle metrics. Machine Learning Engineer questions skew toward recommender and ranking work (place and friend recommendation, sparse-matrix ops, linear-regression derivations, newsfeed dislike models). Data Engineers focus on data modeling, ETL, capacity calculations, reservations/utilization queries, and production SQL/Python tasks. For interview preparation, prioritize timed coding practice, system-design templates, rigorous SQL drills (joins/CTEs/aggregation), clear A/B-testing frameworks, and concise STAR behavioral stories tied to measurable impact.

1.2k Questions 1 Company07.06.2026
Showing 20 results
Role
Meta logo
Meta
Hard
Data Scientist

Define metrics for high-quality notifications

Define metrics for high-quality notifications Context You are a Data Scientist partnering with a product team at Facebook/Meta that owns push/in-app n...

Analytics & Experimentation
3
0
36 people solved
Jul 27, 2025
Meta logo
Meta
Medium
Software Engineer

Convert 32-bit integer to hexadecimal

Problem Given a 32-bit signed integer num, return its hexadecimal representation as a string. Requirements - Use lowercase letters a-f. - Do not inclu...

Coding & Algorithms
1
0
25 people solved
Dec 15, 2025
Meta logo
Meta
Medium
Machine Learning Engineer

Compute nested depth sum and grid distance

Problem A: Weighted sum of integers in a nested list You are given a nested list structure that may contain integers or other nested lists. Define the...

Coding & Algorithms
9
0
67 people solved
Dec 15, 2025
Meta logo
Meta
Easy
Software Engineer

Merge overlapping time intervals

Problem You are given a list of closed intervals intervals, where each interval is [start, end] and start <= end. Merge all intervals that overlap and...

Coding & Algorithms
2
0
34 people solved
Dec 15, 2025
Meta logo
Meta
Medium
Software Engineer

Solve binary tree, grid, and heap tasks

Solve binary tree, grid, and heap tasks Answer the following independent coding tasks: 1) Range sum in a BST: Given the root of a binary search tree a...

Coding & Algorithms
3
0
31 people solved
Jul 17, 2025
Meta logo
Meta
Medium
Software Engineer

Discuss achievements, ambiguity handling, growth, and conflict management

Discuss achievements, ambiguity handling, growth, and conflict management Behavioral & Leadership Interview Prompts (Software Engineer — Onsite) Conte...

Behavioral & Leadership
1
0
25 people solved
Jul 15, 2025
Meta logo
Meta
Medium
Software Engineer

Merge two sorted arrays in-place

Merge two sorted arrays in-place You are given two sorted integer arrays nums1 and nums2, both sorted in non-decreasing order. - nums1 has length m + ...

Coding & Algorithms
3
0
42 people solved
Jul 15, 2025
Meta logo
Meta
Hard
Data Scientist

Evaluate Success Metrics for Facebook Groups and New Features

Evaluate Success Metrics for Facebook Groups and New Features You are evaluating Facebook Groups and a possible new local feature called Circle, a lig...

Analytics & Experimentation
12
0
35 people solved
Jul 12, 2025
Meta logo
Meta
Easy
Data Scientist

Probability a negative review came from a lazy reviewer

Bayesian Posterior: Negative Review and Lazy Reviewer Reviewer types in the population: - Lazy reviewers are 20% of reviewers and never leave negative...

Statistics & Math
28
0
66 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Data Scientist

Determine Success Metrics for New Group Video-Call Feature

Determine Success Metrics for a New Group Video-Call Feature Meta is exploring a new group video-call feature. You need to estimate demand before laun...

Analytics & Experimentation
32
0
96 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Data Scientist

Evaluate Social Media's Brand Advertising Effectiveness

Evaluate Social Media's Brand Advertising Effectiveness A retailer runs both direct-response ads and brand-awareness ads. Leadership suspects social-m...

Analytics & Experimentation
76
0
191 people solved
Jul 12, 2025
Meta logo
Meta
Easy
Data Scientist

Calculate Posterior Probability of Flagged User Being Bad Actor

Calculate Posterior Probability of a Flagged User Being a Bad Actor A platform runs a binary classifier that flags users who might be bad actors. You ...

Statistics & Math
107
1
271 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Annotating and forecasting a long‑tail distribution

Annotating and Forecasting a Long-tail Distribution You are analyzing daily share counts across many pages on a social platform. The cross-sectional d...

Statistics & Math
23
0
49 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Model Unique Recipients with Poisson Distribution and Test Fit

Model Unique Recipients with a Count Distribution and Test Fit You need to model the distribution of the number of unique recipients each caller conta...

Statistics & Math
45
0
62 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Software Engineer

Find kth largest element in array

Question Given an unsorted array of integers nums and an integer k, return the k-th largest element in the array. The 1st largest element is the maxim...

Coding & Algorithms
3
0
56 people solved
Dec 8, 2025
Meta logo
Meta
Medium
Software Engineer

Sum numbers formed by root-to-leaf paths

You are given the root of a binary tree where each node contains a single digit from 0 to 9. Each root-to-leaf path represents a number obtained by co...

Coding & Algorithms
4
0
38 people solved
Dec 8, 2025
Meta logo
Meta
Medium
Software Engineer

Merge overlapping intervals

You are given a list of closed intervals on the number line, where each interval is represented as a pair of integers [start, end] with start <= end. ...

Coding & Algorithms
4
0
47 people solved
Dec 8, 2025
Meta logo
Meta
Medium
Product Manager

Hiking App: Design, Metrics, and Go-to-Market

Hiking App: Design, Metrics, and Go-to-Market You are a Product Manager asked to design a consumer hiking mobile app from zero. Assume iOS and Android...

Product / Decision Making
16
0
65 people solved
Jul 4, 2025
Meta logo
Meta
Medium
Product Manager

Program Execution Deep Dive

Program Execution Deep Dive Describe an end-to-end program or product you led as a Product Manager. The interviewer wants to understand how you captur...

Product / Decision Making
10
0
58 people solved
Jul 4, 2025
Meta logo
Meta
Medium
Product Manager

Meta Pay: Metrics & Prioritization

Product Metrics and Prioritization Prompt: Meta Pay Assume you are the PM for Meta Pay, the consumer payments system used across Meta apps such as Mes...

Product / Decision Making
15
0
56 people solved
Jul 4, 2025

Frequently Asked Questions

How difficult are Meta interview questions?
Meta interview questions span a wide difficulty range because they must screen candidates from entry to senior levels across many functions. Expect coding rounds to map to medium-to-hard algorithmic problems that appear in top 100 problem lists for software engineers, and expect data roles to face challenging SQL, experiment diagnosis, and product-analytics problems that require clean metric definitions. Machine learning roles emphasize recommendation and ranking tradeoffs and model complexity, while data engineers encounter large-scale ETL and modeling puzzles. Difficulty scales with level: entry hires see clearer, bounded problems; senior hires face ambiguous tradeoffs and system-wide thinking.
What is Meta's interview process and where do these questions appear?
Meta typically runs a multi-stage process: recruiter screen, one or two technical screens or an online assessment, a full loop of onsite-style interviews, then debrief, committee review, and offer. The full loop mixes coding, system or product design, role-specific technical rounds, and behavioral interviews. Software-engineer candidates spend most time on coding and design; data scientists focus on SQL, experimentation, and product analytics; machine-learning engineers see modeling and recommendation design; data engineers handle SQL, data modeling, and pipeline questions; PMs get product-design and analytics probes. In 2025–2026 some teams pilot AI-enabled coding rounds.
How should I structure a preparation timeline for a Meta interview?
A focused six-week plan works well: weeks one and two cover fundamentals—data structures, algorithms, SQL basics, and experiment design; weeks three and four emphasize timed problem practice, mock phone screens, and role-specific cases (A/B diagnosis for data scientists, model design for MLEs, ETL modeling for data engineers); week five concentrates on system or product design and behavioral storytelling; week six is for full mock loops, timing, and refining communication. Practice with realistic tools, simulate loop pacing, and schedule a debrief after each mock to iterate on clarity, edge-case handling, and time management.
Which technical subtopics are most commonly tested for each role at Meta?
For Data Scientist interviews the recurring technical themes are product-metric definition, diagnosing experiment and spend drops, counting multi-account interactions, SQL for multi-entity metrics, and ranking or recommendation evaluation such as shop ad ranking. Software-engineer questions frequently focus on timestamped state and versioned systems, leaderboards and ranking, maze/graph traversal and tree/array transforms, delimiter and CSV parsing, and scalable search or flight-search style designs. Machine-learning engineers see place and friend recommendation design, sparse-matrix operations, ranking/loss choices, and feed dislike or personalization models. Data engineers repeatedly face entity modeling for feed and booking data, SQL analytics for utilization and reservations, and capacity-aware aggregation challenges.
What standout tips and common pitfalls should I watch for in Meta interviews?
Start interviews by clarifying requirements and expected outputs, then propose measurable success metrics; this prevents misaligned solutions. For coding, think aloud, handle edge cases, state complexity up front, and write a couple of quick tests. In design rounds quantify load, storage, and tradeoffs rather than vague features. Data roles must define metrics, guardrails, and experiment assumptions before jumping to analysis; common pitfalls are ambiguous metric definitions, peeking at tests, and ignoring instrumentation limits. For AI-assisted coding rounds, use the assistant to accelerate boilerplate but validate logic and corner cases yourself. Finish each answer with a concise summary of impact and tradeoffs.

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