LinkedIn Data Engineer Interview Guide

LinkedIn is not just another big tech company that uses Kafka; they wrote it, along with Pinot, Samza, Gobblin, Brooklin, and Azkaban. Their loop centers uniquely on graph data and event streaming: nearly every problem has a graph dimension (feed ranking by connection strength, job recommendations by network proximity, ad targeting on professional-graph signals) and expects a real-time serving layer alongside batch. Interviewers assume you reason about trillions-of-events scale from the start, not as an afterthought.

The LinkedIn interview timeline

First contact to offer, stage by stage, with how long each round runs and roughly when it lands.

1
wk 030 min
Recruiter Screen
2
wk 1-260 min
Technical Phone Screen
3
wk 34 to 5 hours
Onsite Loop

Typical LinkedIn loop, first contact to offer. Week estimates are approximate and vary by team and scheduling.

LinkedIn data engineer interview process

The loop stage by stage, from recruiter call to offer.

01
Recruiter Screen
Initial call about your experience and interest in LinkedIn. The recruiter evaluates your background with large-scale data infrastructure and distributed systems. LinkedIn invented Kafka and has contributed Pinot, Gobblin, and Brooklin to open source. They look for candidates who have worked with high-throughput data systems and understand the challenges of processing data from nearly a billion members.
- ▸Mention experience with event streaming, especially Kafka, since LinkedIn created it
- ▸LinkedIn is part of Microsoft but operates independently; ask about the specific team (Feed, Ads, Talent Solutions, Data Infrastructure)
- ▸Show interest in infrastructure that serves both real-time and batch analytics
02
Technical Phone Screen
SQL and coding problems set in a professional network context. Expect questions about connection graphs, engagement metrics, and content distribution. LinkedIn phone screens test standard SQL plus the ability to reason about graph-like data structures in relational tables. You may also get a Python coding problem focused on data processing.
- ▸Practice SQL with graph data: mutual connections, degrees of separation, influence metrics
- ▸Be ready for window functions on engagement data: time-series, ranking, and sessionization
- ▸LinkedIn uses Java heavily, but Python is accepted for interview coding
03
Onsite Loop
4 to 5 rounds covering system design, SQL deep dive, coding, data modeling, and behavioral. System design at LinkedIn involves real-time feed processing, ad targeting pipelines, and large-scale graph analytics. The behavioral round evaluates collaboration and alignment with LinkedIn's culture of transformation, integrity, and acting like an owner.
- ▸System design should reference Kafka for messaging, Pinot for real-time analytics, and Spark for batch
- ▸LinkedIn's data platform processes trillions of events daily; every answer should acknowledge this scale
- ▸The behavioral round tests ownership: describe situations where you drove outcomes without being directed

What the LinkedIn loop tests: domains and difficulty

Our prediction of the question mix by domain and difficulty for this company's data engineer loop, from live listings and interview reports.

By domain

SQL

57%

Python

43%

By difficulty

Easy

50%

Medium

29%

Hard

21%

The domain and difficulty mix we predict for a LinkedIn data engineer loop, across 14 problems. It updates as more LinkedIn data lands.

Updated Jul 24, 202614 predicted LinkedIn problems

4 real LinkedIn interview questions

Reported by candidates from real loops, tagged by domain, round, level, and year. Expand for what the round is scoring.

SQLL5 · 2025

Identify users whose personal profile followers exceed their employer company's follower count

Onsite · sql

Tables: personal_profiles(profile_id, name, followers, employer_id), company_pages(company_id, name, followers). JOIN personal_profiles to company_pages on employer_id = company_id, then filter WHERE personal_profiles.followers > company_pages.followers. Tests understanding of join conditions and comparison filtering.

PythonL5 · 2025

leetcode 3: Longest Substring Without Repeating Characters

Online assessment

PythonL5

leetcode 3: Longest Substring Without Repeating Characters

Technical · screen

Behavioral / mixedunknown · 2023

Centiva Capital | NY | SWE/ Data Engineer | All rounds interview experience

Onsite · sql

I was reached out by a recruiter on LinkedIn. After couple of weeks I was asked to do a round with the hiring manager. The round with HM was basically an intro to CC and little bit about myself. It lasted half hour and I felt good about CC. Next round again I wasn\'t sure what kind of an interview round it was going to be but the recruiter told me it was probably going to be a behavorial one. In this round a teammate asked me about my past experience, team size, team management etc. It also went well and I felt good about it. Next round was with another senior teammate and I hadn\'t prepared…

How candidates rate the LinkedIn loop

How hard candidates rated the loop and how they felt, summarized across the reports below.

How hard candidates rated it

Easy

20%

Medium

20%

Hard

60%

5 rated reports

How candidates felt

Positive

Neutral

100%

Negative

1 rated LinkedIn report

Recent LinkedIn interview reports

Candidate accounts of the loop, each with its date, level, difficulty, and outcome. Scroll the feed.

5 candidate interview reports

real candidate submissions

No offerAverage difficulty· midSep 2025

After talking with the hr, I was scheduled for one hour coding round, with 25 min algorithm questions leetcode question 3: Longest Substring Without Repeating Characters, and 25 mins sql with followup

· seniorMar 2025

Interviews: ##### Online Assessment: Hackerrank (2 hours) * Two Leetcode Medium questions on DSA * Two SQL medium questions on SQL I was able to solve all 4 within the time with all test-cases passed. ##### Round 0: Telephonic Screening (1 hour) Two interviewers joined the call from the hiring team, Optimization of code/queries is expected. So always strike for the optimal solution * One Leetcode Medium question on DSA (with follow ups) * One SQL Medium question on window/rank (with follow ups) It went well, so I was certain that I will get a call back. ## On Site Interviews After getting the…

· seniorSep 2024

Recently gave interview for Linkedin. It had 1 OA + 6 rounds. **OA** * 1 Medium + 1 Hard Leetcode Question. * 2 SQL medium question. ( Use of rank and row_number questions type) **Round 1: Phone Screening** * This was screening round and elimination round. * 1 Coding: Find kth largest in tree. * 1 SQL: Medium + Followup on SQL. After this round got call from HR that I cleared screening round and after 1 week next rounds were schedule. **Round 2: SQL Round** * This round involved multiple SQL questions\u2014four in total (including followup) * One question was asked to code in Python/R. I used…

No offerEasy· principalMar 2024

Status: Tier 2 Btech Position: Staff Engineer at a product company Location: India Date: January, 2024 Technical phone screen (1 hour): House robber-2 + bunch of simple sql questions Onsite (6 rounds): R1 (SQL + python coding) 2 questions. You need to come up with SQL solution for both and then code the equivalent python solution. R2: Manager round Bunch of behavioral questions like any other company. The manager seemed to be over confident and called me a liar in the middle of the interview. Now, linkedin is a great company and talented folks work there. But with your designation, you should…

· seniorMay 2021

``` LinkedIn | Senior Data Engineer - Data Science| Seattle, WA | May 2021 | Phone interview 1. Coding Description: Given an m x n 2D binary grid which represents a map of \'1\'s (land) and \'0\'s (water), return the number of islands. An island is surrounded by water and is formed by connecting adjacent lands horizontally or vertically. You may assume all four edges of the grid are all surrounded by water. Examples: Input: grid = [ ["1","1","1","1","0"], ["1","1","0","1","0"], ["1","1","0","0","0"], ["0","0","0","0","0"] ] Output: 1 Input: grid = [ ["1","1","0","0","0"],…

Try a LinkedIn-style SQL round

Find every user active on 3 or more CONSECUTIVE days. This gaps-and-islands shape shows up in nearly every DE SQL round. Edit the query and run it against the seed data.

	/* Users active on 3+ consecutive days. */
	/* Hint: date minus a per-user ROW_NUMBER is constant within a streak. */
	WITH streaks AS (
	SELECT
	user_id,
	activity_date,
	activity_date - CAST(
	(ROW_NUMBER() OVER (
	PARTITION BY user_id
	ORDER BY activity_date
	))
	AS INT
	) AS grp
	FROM user_sessions
	)

	SELECT
	user_id
	FROM streaks
	GROUP BY user_id, grp
	HAVING COUNT(*) >= 3

Practice the LinkedIn loop

Problems our platform predicts for this company's interview, grouped by round. Drill the shapes that actually come up.

SQL3 to drill

High Volume Batch Jobs

Python3 to drill

Merge IntervalsSolve →

EASY~10m

The Repeat Offenders

Frequency CountingSolve →

All LinkedIn-style problems →

Common mistakes in LinkedIn DE interviews

The patterns that sink otherwise strong candidates here.

Treating LinkedIn like a generic FAANG interview

LinkedIn's data challenges are uniquely centered on graph data and event streaming. Candidates who prepare with generic SQL and system design problems miss the core of what LinkedIn tests. Every answer should connect back to the professional graph, Kafka event pipelines, or real-time analytics on member activity.

Not understanding the tools LinkedIn created

LinkedIn built Kafka, Pinot, Samza, Gobblin, Brooklin, and Azkaban. When you reference these in system design, you should know why LinkedIn created each one and what problem it solved. Saying 'I would use Kafka' without understanding partitioning, consumer groups, or exactly-once semantics signals shallow preparation.

Ignoring the graph dimension of every problem

Nearly every data problem at LinkedIn has a graph component. Feed ranking depends on connection strength. Job recommendations use network proximity. Ad targeting leverages professional graph signals. Candidates who solve problems using only flat relational thinking miss the deeper answer LinkedIn interviewers expect.

Designing for batch when LinkedIn needs real-time

LinkedIn serves real-time feed, real-time notifications, and real-time ad bidding. System designs that rely entirely on batch processing miss the mark. Always include a streaming layer (Kafka + Samza or Kafka Streams) and a real-time serving layer (Pinot or Venice) alongside batch pipelines.

Confusing LinkedIn's culture with Microsoft's

Despite the acquisition, LinkedIn maintains its own engineering culture, leveling system (mapped to Microsoft levels), and interview process. Preparing for Microsoft's 'growth mindset' behavioral questions instead of LinkedIn's 'transformation, integrity, act like an owner' values is a common misstep.

LinkedIn-specific preparation tips

Tactical advice for the dimensions this company weighs.

LinkedIn invented Kafka and thinks in events

Kafka was born at LinkedIn to solve their real-time data pipeline challenges. Interviewers expect you to understand Kafka deeply: topics, partitions, consumer groups, exactly-once semantics, and when to use compacted topics. Event streaming is the foundation of LinkedIn's data architecture.

Graph data is central to LinkedIn's business

The professional graph (nearly a billion members and their connections) drives feed ranking, job recommendations, and ad targeting. Be ready to discuss graph traversal, mutual connections, influence scoring, and how to store and query graph data at scale.

Know LinkedIn's open-source ecosystem

Beyond Kafka, LinkedIn created Apache Pinot (real-time analytics), Apache Gobblin (data ingestion), Brooklin (change data capture), and Samza (stream processing). Understanding what each tool does and why LinkedIn built it shows genuine interest.

Scale is measured in trillions of events

LinkedIn processes trillions of data events daily across feed, messaging, ads, and talent solutions. When designing systems, think in terms of millions of events per second, petabytes of storage, and sub-second query latency for real-time features.

Microsoft ownership does not change the interview

LinkedIn operates independently within Microsoft. The interview process, culture, and tech stack are LinkedIn-specific. Do not prepare for a Microsoft-style interview; focus on LinkedIn's infrastructure-heavy, event-driven engineering culture.

What LinkedIn is really evaluating

The signals behind the questions. Shape every answer around these.

LinkedIn created the modern data streaming ecosystem

Apache Kafka was invented at LinkedIn in 2011 to solve their real-time data pipeline challenges. Apache Pinot was built for real-time OLAP queries on member activity. Apache Samza was created for stream processing. This is not a company that adopted open-source tools; they wrote the tools the rest of the industry uses. Interviewers expect you to understand this lineage.

The professional graph is the product

LinkedIn's core asset is a graph of nearly a billion professionals and their relationships. Every product surface (feed, jobs, recruiter tools, ads, learning) depends on this graph. Data engineers at LinkedIn work with graph algorithms, connection strength signals, and network-aware data models that most companies never encounter.

Microsoft parent company means Microsoft leveling

LinkedIn maps to Microsoft's leveling system. Compensation includes Microsoft RSUs on a 4-year vest with annual refreshes. The corporate structure provides stability and competitive pay, but the engineering culture and tech stack remain distinctly LinkedIn.

Scale that few companies match

LinkedIn processes trillions of events per day across hundreds of Kafka clusters. The professional graph has billions of edges. Pinot serves millions of analytical queries per second. When interviewers ask you to design a system, they expect you to reason about this scale from the start, not treat it as an afterthought.

LinkedIn compensation and culture

The numbers, tech stack, and team structure live on the company overview.

Data engineering at LinkedIn→

The role, comp by level, tech stack, teams, and culture signals.

LinkedIn DE interview FAQ

How many rounds are in a LinkedIn DE interview?+

Typically 5 to 6: recruiter screen, technical phone screen, and 3 to 4 onsite rounds covering SQL, system design, coding, and behavioral. Some teams add a data modeling round. The full process takes 3 to 5 weeks from first contact to offer.

Does LinkedIn test Kafka knowledge directly?+

Not always as a coding exercise, but Kafka concepts are central to system design discussions. Know partitioning strategies, consumer group rebalancing, exactly-once processing, and when to use Kafka Streams vs a separate processor like Flink or Samza.

What programming languages does LinkedIn use?+

Java is the primary language for backend and data infrastructure. Python is used for analytics, ML pipelines, and scripting. Scala appears in Spark jobs. For interviews, Python and Java are both accepted. SQL is tested in a dedicated round.

How does LinkedIn's DE interview compare to Microsoft's?+

LinkedIn interviews are more infrastructure-focused and emphasize real-time systems, event streaming, and graph data. Microsoft DE interviews lean toward Azure services and growth mindset culture. Despite the corporate relationship, the interviews are distinct.

What is LinkedIn's leveling system for data engineers?+

LinkedIn maps to Microsoft levels. Most external hires for DE roles land at the Senior SDE band. Leveling is determined during the interview process and directly impacts compensation.

Which LinkedIn teams hire the most data engineers?+

Data Infrastructure (the team behind Kafka, Pinot, and Venice) and Ads and Monetization are the largest DE employers. Feed and Content and Talent Solutions also hire heavily. Each team has different technical emphases, so ask your recruiter about the specific team during the first call.

Do I need to know graph algorithms for the interview?+

You do not need to implement Dijkstra from memory, but you should be comfortable reasoning about graph traversal in SQL (self-joins on connections tables, mutual connection queries) and in system design (how to compute recommendations from a billion-node social graph). Graph thinking is expected, not optional.

What is the compensation structure at LinkedIn?+

Total compensation includes base salary, Microsoft RSUs (4-year vest with annual refresh), and a signing bonus. RSUs make up a significant portion of senior-level comp. Annual performance reviews determine refresh grants.