How to Prepare for LinkedIn Interview 2026 — Complete Guide
Step-by-step preparation guide for LinkedIn interviews. Covers what to study, how long to prepare, top resources, and 12 real questions reported by engineers who cleared the LinkedIn process.
Step 1 — Understand what LinkedIn actually tests
Based on 12 questions reported by engineers, LinkedIn focuses on: System Design, Behavioral, Domain, Data Engineering / Airflow, System Design / Feed, Java / Concurrency, SQL / Analytics.
Step 2 — Study the right topics in order
- System Design — 4 questions. Start here — highest weight in technical rounds.
- Behavioral — 2 questions. Second priority after the first topic.
- Domain — 2 questions. Cover once the first two topics are solid.
- Data Engineering / Airflow — 1 questions. Cover once the first two topics are solid.
- System Design / Feed — 1 questions. Cover once the first two topics are solid.
- Java / Concurrency — 1 questions. Cover once the first two topics are solid.
- SQL / Analytics — 1 questions. Cover once the first two topics are solid.
Step 3 — Practice with real questions
These are the most upvoted questions engineers report being asked at LinkedIn:
How does Airflow handle backfilling? Design a DAG that safely backfills 90 days of missing data without overloading the database.
Design LinkedIn's feed. Each user sees posts from their connections, pages they follow, and sponsored content. Posts must be ranked by relevance. Cover: fan-out-on-write vs fan-out-on-read, feed generation for users with 30k connections, and freshness.
Design LinkedIn's "People You May Know" feature. How do you compute second and third-degree connections for 900 million users efficiently? Discuss graph storage (adjacency list vs matrix), batch vs real-time computation, and how to refresh recommendations when someone adds a new connection.
LinkedIn uses Kafka extensively. Explain how you would design the Kafka topic structure for LinkedIn's activity events (profile view, connection request, message sent). Cover: topic naming, partitioning strategy, consumer group design, and exactly-once semantics.
Design LinkedIn's job recommendation engine. Given a user's profile, skills, and activity, rank the most relevant open positions. Cover candidate retrieval, feature engineering (skills overlap, company affinity, recency), and how you A/B test ranking changes at scale.
Step 4 — Analyse the job description
Paste the exact LinkedIn JD into Stepkai's AI tool. It will extract the competencies being tested, score your current readiness, and generate a personalised day-by-day plan targeting your specific gaps — not a generic checklist.
Step 5 — Mock interview
Practice answering LinkedIn questions with AI grading. Get instant feedback on depth, correctness, and communication before the real thing.
Start practising →