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

Track Success and Guardrail Metrics for Push Notifications

Track Success and Guardrail Metrics for Push Notifications You are designing and evaluating a new push-notification feature for a travel-recommendatio...

Analytics & Experimentation
93
0
378 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Determine Features for Effective Hashtag Recommendations

Hashtag Recommendation System Design You are designing a hashtag recommendation system for a social-media platform. Given a user composing post conten...

Machine Learning
105
1
279 people solved
Jul 12, 2025
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Meta
Easy
Data Scientist

Calculate Expected Day for First Selection in Sampling

Expected Day of First Selection in Daily Sampling There are 1,000 people. Each day, 10 distinct names are selected uniformly at random. Day counting s...

Statistics & Math
27
0
67 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist Locked

Determine Probability of Shared Videos in Recommendations

Meta statistics and product prompt on video recommendation overlap, covering combinations, probability of shared videos, expected intersection size, s...

Statistics & Math
58
0
113 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Data Scientist

Implement Clustered Sampling to Mitigate Network Effects in Testing

Implement Clustered Sampling to Mitigate Network Effects in Testing You are planning an A/B test for a new recommendation algorithm in a networked pro...

Analytics & Experimentation
24
0
99 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Machine Learning Engineer

Design a recommendation system

System Design: Large-Scale Home-Feed Recommendation System Problem Design a large-scale recommendation system for a consumer app's home feed. Describe...

System Design
7
0
59 people solved
Sep 6, 2025
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Meta
Hard
Data Engineer

Design tables for event-driven metrics

Design a Relational Schema for Consumer-App Event Analytics Context and Assumptions You are designing the event store for a high-volume consumer app. ...

System Design
7
0
73 people solved
Sep 6, 2025
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Meta
Medium
Software Engineer

Find shortest path and compare BFS vs DFS

Given an unweighted graph (directed or undirected), explain how to find the shortest path between two nodes without writing code. Describe the algorit...

Coding & Algorithms
5
0
50 people solved
Sep 6, 2025
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Meta
Medium
Data Engineer

Design a scalable dimensional model

Design a Dimensional Model for Transactional Analytics (Concrete Example Included) You are building a star-schema in a cloud data warehouse for near r...

System Design
13
0
91 people solved
Sep 6, 2025
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Meta
Hard
Software Engineer

Design a scalable banking system

System Design: Core Banking Platform Problem Design a banking system that supports: - Account creation - Balance inquiry - Deposit and withdrawal - At...

System Design
3
0
44 people solved
Sep 6, 2025
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Meta
Hard
Software Engineer

Design a bank system with transfers

Single-Process BankSystem: APIs, Data Model, Algorithms, and Edge Cases Context and Assumptions You are asked to design and implement a single-process...

System Design
4
0
64 people solved
Sep 6, 2025
Meta logo
Meta
Medium
Software Engineer

Explain a recent project and measured impact

Behavioral & Leadership: High-Impact Project Deep Dive You are interviewing for a software engineering role. Provide a concise, metrics-driven walkthr...

Behavioral & Leadership
4
0
49 people solved
Sep 6, 2025
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Meta
Medium
Data Engineer

Define and analyze product metrics

Product Analytics Case: Short‑Form Video Feed Context: You are evaluating a short‑form video feed feature inside a large social app where users swipe ...

Analytics & Experimentation
9
1
61 people solved
Sep 6, 2025
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Meta
Hard
Software Engineer

Design an online auction platform

System Design: Real-Time English Auction Platform Goal Design an online auction platform that supports English-style auctions with: - Reserve price - ...

System Design
10
0
80 people solved
Sep 6, 2025
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Meta
Medium
Software Engineer

Derive max distinct frequencies for n items

Maximum Number of Distinct Frequency Counts in an Array Context You are given an array of length n ≥ 1 whose elements are arbitrary integers (values m...

Statistics & Math
9
0
74 people solved
Sep 6, 2025
Meta logo
Meta
Hard
Software Engineer

Design an Instagram-like photo-sharing platform

System Design: Instagram-like Photo and Short-Video Platform Goal Design an Instagram-like platform for photos and short videos. The design should cov...

System Design
4
0
45 people solved
Sep 6, 2025
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Meta
Medium
Site Reliability Engineer Locked

Implement a Streaming VMStat Alert

This question evaluates streaming data processing skills, sliding-window counting algorithms, and efficient online state management for metric monitor...

Coding & Algorithms
5
0
73 people solved
Apr 6, 2026
Meta logo
Meta
Medium
Data Scientist

Count unconnected posts and reactions

You are analyzing a newly launched feed feature intended to improve engagement by showing more unconnected content. Assume the following tables: - pos...

Data Manipulation (SQL/Python)
21
2
200 people solved
Apr 5, 2026
Meta logo
Meta
Medium
Software Engineer Locked

Solve delimiter and CSV tasks

This question evaluates skills in string parsing and bracket balancing, CSV/file parsing and joining, computation of derived values, and sorting, emph...

Coding & Algorithms
6
0
52 people solved
Apr 5, 2026
Meta logo
Meta
Medium
Software Engineer Locked

Design an Instagram-like auction platform

This question evaluates expertise in large-scale system design, including distributed systems, data modeling, real-time messaging, consistency models,...

System Design
1
0
23 people solved
Oct 30, 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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