Data Engineering at LinkedIn
Roles, Comp & Culture
An L6 staff data engineer at LinkedIn sits around $447K total comp from 29 verified salary datapoints. The ladder runs from about $236K at entry up to $447K. LinkedIn pays data engineers above other Technology companies. Reviews put them at 3.8 on Glassdoor, a little above the middle of the pack. LinkedIn employees are stressed and employee happiness is trending down over the past year. Layoff risk scores low for the next 30 days.
Technology · Sunnyvale, IE
live data · August 2, 2026
DE total comp
$447K median
L6 · staff level · $334K–$479K · 8 verified datapoints
Hiring now
No open DE roles
tracked daily
Team happiness
Stressed
employee happiness
Layoff risk (30d)
Low
Employee sentiment
3.8 / 5
MixedEmployees
11–50
LinkedIn data engineer compensation
Each level's figure is the median of individual LinkedIn offers at that level, so it reflects a typical outcome rather than an average pulled up by a few large packages. Total comp counts base salary plus equity and bonus annualized over the vest, and the range shown is the middle half of offers, with the top and bottom quarters trimmed off.
What the LinkedIn signals mean
LinkedIn operates independently within Microsoft, maintaining its own engineering culture, leveling system, and interview process. The behavioral round evaluates alignment with LinkedIn's values of transformation, integrity, and acting like an owner, distinct from Microsoft's growth-mindset framing.
LinkedIn employee sentiment, tracked weekly
Employee happiness for data engineers over the past year, so you can see which direction it is moving, not just where it sits today.
Recent LinkedIn events
Layoffs, leadership changes, and other major moves at the company, with dates.
- LayoffJul 2026~66 roles cut
- LayoffMay 2026Layoff
- LayoffMay 2026Layoff
- LayoffMay 2026Layoff
Notable company events we track, with dates.
Practice for the LinkedIn loop
Round by round, the problems our model predicts for this company's interview. Rehearse the shapes their panels keep returning to.
Data engineering teams at LinkedIn
Which team you interview for shapes the questions. The main DE surfaces:
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.
What makes LinkedIn different
The things about this company that should shape every answer you give.
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.
Preparing for the LinkedIn loop
The round-by-round process, example questions, and prep plan are on the interview guide.
LinkedIn data engineer roles by level
Level-specific pages: the comp, the bar, and what the loop tests at each seniority.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Compare LinkedIn with other data engineering employers
How the role, pay, and loop stack up against peer companies.
Prepare at LinkedIn interview difficulty
- 01
Reading a solution is not the same as writing one
Every engineer who has frozen on a query they had read a dozen times knows the gap. The only preparation that closes it is producing the answer yourself, under time, before the interview does it for you
- 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
5 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