LinkedIn Data Engineer Interview
LinkedIn's Data Engineer loop (short) emphasizes Balanced between Microsoft cultural influence and its own member-graph data focus. Candidates who clear it demonstrate shipped production pipelines end-to-end and can debug them when they break backed by roughly 2-5 years.
Compensation
$155K–$190K base • $240K–$320K total
Loop duration
3 hours onsite
Rounds
4 rounds
Location
Sunnyvale, NYC, Chicago, Dublin, Bangalore
Tech stack
What LinkedIn data engineers actually use
Tools and languages mentioned most often in LinkedIn's currently-active data engineer postings. Each chip links to an interview prep page for that tool.
Round focus
Domain concentration by round
What each LinkedIn round typically tests, weighted across 2 live data engineer postings. The bars show the relative emphasis of each domain.
Online Assessment
Phone Screen
Onsite Loop
Walk into LinkedIn knowing the Python pattern they'll test.
Practice problems
LinkedIn data engineer practice set
Practice sets surfaced for LinkedIn data engineer candidates by the same model that reads their job postings. Each card opens a working coding environment.
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.
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.
Users Who Churned in February
Find all users who had sessions in January {{YEAR}} but none in February {{YEAR}}.
Data Quality Report
Given a list of record dicts, return a dict per column name with 'null_count' and 'non_null_count'. Consider a value null when it is Python None.
Rolling 7-day active users
Count distinct users active in the trailing 7 days for each date. Product analytics staple.
The Zigzag Encoder
The message snakes its way across the rails.
Pulled from debriefs where Python parsing was the gate.
The loop
How the interview actually runs
01Recruiter screen
30 minLinkedIn has strong internal mobility and an emphasis on career trajectory. Recruiters ask about long-term motivations.
- →Mention interest in specific verticals: Growth, Ads, Learning, Talent Solutions, Premium
- →LinkedIn's member-graph data is distinctive, any graph-data experience helps
- →Ask about hybrid work expectations early, varies by team
02Technical phone screen
60 minSQL + Python. Graph-oriented and member-activity problems come up often: connections, engagement feeds, skill graphs.
- →Practice graph-flavored SQL: shortest paths, N-degree connections, PageRank-style computations
- →Python round often involves simple data structures, not algorithms
- →Mention Pinot or Samza experience if you have it. LinkedIn open-sourced both
03Onsite: system design
60 minDesign a data-intensive LinkedIn feature: feed ranking pipeline, member search indexing, notification delivery, engagement analytics.
- →Online/offline split: real-time feed scoring + batch feature computation
- →LinkedIn's open-source stack is fair game in design answers
- →Discuss cross-region replication. LinkedIn is globally distributed
04Onsite: culture + growth
60 minBehavioral round with Microsoft-influenced growth-mindset framing. LinkedIn interviewers also assess cultural values: members first, trust, transformation.
- →Member-first framing: how does your data work serve LinkedIn members?
- →Trust stories: data privacy, member-facing accuracy
- →Growth-mindset language still applies here, inherited from Microsoft
Level bar
What LinkedIn expects at Data Engineer
Pipeline ownership
Mid-level DEs own pipelines end-to-end. Interviewers expect stories about designing, deploying, and maintaining a data pipeline that has been in production for 6+ months.
SQL + Python or Spark fluency
SQL is the floor. Most teams also expect fluency in either Python for data manipulation (pandas, airflow DAGs) or Spark for larger-scale processing.
On-call debugging
You should have concrete stories about production incidents: what alert fired, how you diagnosed, what you fixed, and what post-mortem action you owned.
LinkedIn-specific emphasis
LinkedIn's loop is characterized by: Balanced between Microsoft cultural influence and its own member-graph data focus. Calibrate your preparation to that, generic FAANG prep will not close the gap on company-specific expectations.
Behavioral
How LinkedIn frames behavioral rounds
Members first
LinkedIn's northstar. DEs are expected to think about members (users), not just metrics.
Trust
LinkedIn's brand is professional credibility. Privacy, accuracy, and reliability are non-negotiable.
Growth mindset
Inherited from Microsoft. LinkedIn interviewers score explicitly on learning from failure.
Relationships matter
LinkedIn's core business. Internally, the company emphasizes strong cross-team relationships.
Prep timeline
Week-by-week preparation plan
Foundations and gap analysis
- ·Do 10 medium SQL problems. Note which patterns feel slow
- ·Write out 2-3 behavioral stories per value, LinkedIn weights this round heavily
- ·Read LinkedIn's public engineering blog for recent architecture patterns
- ·Review your prior production work, pick 3-5 projects you can discuss in depth
SQL and coding fluency
- ·Practice window functions until DENSE_RANK, ROW_NUMBER, LAG, LEAD are reflex
- ·Do 20+ LinkedIn-style problems in their domain
- ·Time yourself: 25 min per medium, 35 min per hard
- ·Record yourself narrating approach aloud, communication is graded
Pipeline awareness and behavioral depth
- ·Review pipeline architecture basics: idempotency, partitioning, backfill
- ·Practice explaining a pipeline you've worked on end-to-end in 5 minutes
- ·Refine behavioral stories based on mock feedback
- ·Do 10 more SQL problems at medium difficulty
Behavioral polish and mock loops
- ·Rehearse every story out loud. Cut to 2-3 minutes each
- ·Run 2 full mock loops with a mid-level DE or coach
- ·Identify your 3 weakest behavioral areas and draft additional stories
- ·Review recent LinkedIn news or earnings call for fresh talking points
Taper and logistics
- ·No new content. Review your notes only
- ·Sleep. Mental energy matters more than one more practice problem
- ·Confirm logistics: laptop charged, shared-doc tool tested, snack and water nearby
- ·Remember: interviewers want to find reasons to hire you, not to reject you
See also
Other guides you'll want
FAQ
Common questions
- How much does a LinkedIn Data Engineer make?
- Total compensation for LinkedIn Data Engineer ranges $155K–$190K base • $240K–$320K total. Ranges shift by team and negotiation.
- How is the Data Engineer loop different from other levels at LinkedIn?
- Data Engineer loops run the same stages as other levels, but interviewers calibrate difficulty to shipped production pipelines end-to-end and can debug them when they break, especially around production pipeline ownership and on-call debugging.
- How long should I prepare for the LinkedIn Data Engineer interview?
- 6-8 weeks is the standard window for a working DE. Less than 4 weeks almost always means cutting the behavioral prep short.
- Does LinkedIn interview data engineers differently than software engineers?
- The tracks diverge. DE at LinkedIn weights SQL and pipeline-design rounds, and interviewers expect specific production data experience that SWE loops don't probe.