Pythonoa· L52025
leetcode 3: Longest Substring Without Repeating Characters
LinkedIn Data Engineer Interview
LinkedIn operates one of the largest professional graphs in the world, processing trillions of events daily across feed, messaging, and talent solutions. They invented Apache Kafka and continue to push the boundaries of real-time data infrastructure. Their DE interviews test event streaming architecture, graph data reasoning, and the ability to build platform infrastructure that serves the entire organization.
Technology · Sunnyvale, IE
live data · June 11, 2026
DE total comp
$330K–$460K
senior level · full ladder below
Hiring now
2 open DE roles
live from career pages
Team happiness
50 / 100 · Neutral
model score from employee signals
Layoff risk (30d)
Moderate
Employee sentiment
3.8 / 5
MixedEmployees
11–50
LinkedIn DE Interview Process
Three stages from recruiter call to offer. The full loop typically takes 3 to 5 weeks.
- 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
Four to five 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
LinkedIn data engineer compensation
Industry ranges by level.
| Level | Base | Total comp |
|---|---|---|
| JuniorL3 | $125K–$155K | $170K–$220K |
| Mid-levelL4 | $155K–$190K | $240K–$320K |
| SeniorL5 | $190K–$235K | $330K–$460K |
| StaffL6 | $225K–$285K | $460K–$640K |
| PrincipalL7 | $270K–$345K |
The LinkedIn data stack
What their data engineers work with day to day. Worth brushing up on the heavy hitters before the loop.
Languages
Tools and platforms
CI/CD1
LinkedIn Teams That Hire Data Engineers
Ask your recruiter which team you are interviewing for. Each team has different technical emphases and interview focus areas.
Feed and Content
News feed ranking, content distribution, viral detection, engagement optimization across nearly a billion members.
Search and Discovery
People search, job search, content search. Relevance ranking and personalization at massive query volume.
Ads and Monetization
Ad targeting pipelines, campaign analytics, conversion tracking, and attribution modeling for LinkedIn Marketing Solutions.
Talent Solutions
Recruiter tools, job matching algorithms, applicant tracking pipelines. The largest revenue driver for LinkedIn.
Data Infrastructure
Core platform: Kafka, Pinot, Venice, Brooklin, Azkaban. The team that builds the tools other teams depend on.
Trust and Safety
Fake account detection, spam filtering, content moderation, and abuse prevention across the platform.
Real LinkedIn interview questions
Reported questions from this company's loops, tagged by domain, round, and level.
SQLonsite sql· L52025
Identify users whose personal profile followers exceed their employer company's follower count
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.
mixedonsite sql· unknown2023
Centiva Capital | NY | SWE/ Data Engineer | All rounds interview experience
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.\nNext 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.\nNext round was with another senior teammate and I hadn\'t…
Pythontechnical screen· L5
leetcode 3: Longest Substring Without Repeating Characters
What Makes LinkedIn Different
LinkedIn is not just another big tech company that uses Kafka. They wrote it. Understanding this distinction is the difference between a good interview and a great one.
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 (L59 through L67). 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.
Common Mistakes in LinkedIn DE Interviews
Patterns that consistently lead to rejections, based on candidate experience reports.
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 each dimension of the interview.
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.
LinkedIn practice set
Problems on the platform tagged and predicted for LinkedIn loops, from live listings and interview reports.
SQLeasy~5 min
Full Customer Order List
Return first_name, last_name, and country for every customer in customers. Sort alphabetically by first_name, then last_name.
Pythonmedium~10 min
Detect Cycle in Sequence
You are given a list of integers where each value at index i is the next index to visit (or -1 to terminate). Starting from index 0, follow the chain and return True if you revisit any index, False otherwise. Out-of-range indices (including -1) count as termination, not a cycle.
SQLeasy~5 min
High Volume Batch Jobs
Surface all batch jobs that processed more than 5000 rows, showing each job's name, priority, and rows processed, ranked from most to fewest.
Pythoneasy~10 min
The Bitwise Judge
Given an integer n (possibly negative), return True if n is even, False if odd. Solve using bitwise operations only - no %, no /, no //.
SQLmedium~5 min
Active Duo
The growth team is building a cross-engagement segment of users who both make purchases and log browsing sessions on the platform. Return a deduplicated list of usernames for users with activity in both areas.
Pythoneasy~10 min
Quantile Calculator
Given a list of numbers and percentile (0-100), return the value at that percentile using linear interpolation. The index is percentile / 100 * (n - 1); if fractional, linearly interpolate between the floor and ceiling indices of the sorted values.
Recent LinkedIn data engineer interview reports
What candidates reported about the loop, in their own words.
1 candidate interview report
real submissions · parsed from Glassdoor
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
Prepare for the interview
01 / Open invite
02min.
Walk into LinkedIn knowing the SQL pattern they'll test.
a LinkedIn SQL query, the same shape a screen would give you.
The diff against expected. Where ties broke. What you missed.
sandbox
1SELECT user_id,
2 COUNT(*) AS sessions
3FROM events
4WHERE ts >= NOW() - INTERVAL '7 day'
5
Execute your solution0.4s avg.
LinkedInInterview question
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. SDE is L59 to L60, Senior SDE is L61 to L62, Staff is L63 to L64, and Principal is L65 to L66. Most external hires for DE roles land at L61 or L62. 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. Total comp ranges from roughly $150K at entry level to $650K+ at Principal.
02 / Why practice
Prepare at LinkedIn Interview Difficulty
- 01
Active recall beats re-reading by 50%
Cognitive-science meta-reviews (Dunlosky et al., 2013) rank practice testing as a top-tier study technique, while re-reading and highlighting rank near the bottom
- 02
76% of hiring managers reject on the coding task, not the resume
From HackerRank's 2024 Developer Skills Report. Candidates who look strong on paper still fail the live screen if they haven't done timed, executable practice
- 03
Five problem shapes cover 80% of data engineer loops
Dedup, sessionization, top-N-per-group, slowly-changing dimensions, partition tricks. Writing the shapes by hand turns the unfamiliar into pattern recognition