LinkedIn Senior Data Engineer Interview
LinkedIn Senior Data Engineer loop: Balanced between Microsoft cultural influence and its own member-graph data focus. Bar at this level: independent technical leadership and cross-team influence. Typical 5-8 years of data engineering experience.
Compensation
$190K–$235K base • $330K–$460K total
Loop duration
4 hours onsite
Rounds
5 rounds
Location
Sunnyvale, NYC, Chicago, Dublin, Bangalore
Tech stack
What LinkedIn senior 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 senior 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 senior data engineer practice set
Practice sets surfaced for LinkedIn senior data engineer candidates by the same model that reads their job postings. Each card opens a working coding environment.
Subscribers Without Premium
Pull basic-plan subscribers who never upgraded to premium from the subscriptions data. The retention team wants to run a winback campaign targeting this group.
The Overlap
Your monitoring system logs server maintenance as `[start, end]` minute ranges, and windows that overlap or sit back-to-back really describe one continuous outage. Collapse the `windows` so any that overlap or touch at an endpoint become a single range, and return them ordered by start time. Two windows touch when one ends exactly where the next begins.
Nth Largest Value
The compensation team needs the second-highest unique metric value in the performance table as a benchmark for setting the next salary band. Return that single value, or NULL if the data does not have enough unique values.
Smooth Latency
Your team is investigating whether certain API endpoints are getting slower over time. For every row in api_calls where latency is not NULL, compute a running average of latency partitioned by endpoint and ordered by call_time, covering all calls up to and including the current one. Return endpoint, latency, and running_avg.
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
04System design (pipeline architecture)
60 minDesign a production pipeline end-to-end: ingestion, transformation, storage, consumers, SLAs, failure modes, backfill strategy, and cost trade-offs. At senior level, you drive the conversation without prompting. Expect follow-ups about scale, cross-team coordination, and operational load.
- →Anchor on the SLA and data shape before diagramming
- →Discuss idempotency, partitioning, and backfill explicitly
- →Estimate cost: 'This pipeline will cost roughly $X/month at this volume'
05Onsite: 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 Senior Data Engineer
Independent technical leadership
Senior DEs drive pipeline designs without engineering manager involvement. Interviewers probe whether you can decompose ambiguous requirements, make architecture trade-offs, and defend your choices under scrutiny.
Cross-team coordination
Senior scope regularly spans multiple teams. Expect scenarios about a downstream team missing an SLA because of a change you made, or negotiating a schema migration with the team that owns the upstream source.
Production operational rigor
Fluent in on-call, alerting, data quality checks, and incident response. Dive-deep stories at this level should include correlating a metric drop to a specific commit or a timezone bug or a subtle ordering issue, not 'I looked at the logs.'
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 system design
- ·Design 5 pipelines on paper: daily aggregation, clickstream, CDC, ML feature store, real-time alerting
- ·For each, write SLA, partition strategy, backfill plan, and cost estimate
- ·Practice with a friend, senior-level system design is 50% driving the conversation
- ·Review LinkedIn's open-source and engineering blog for in-house patterns
Behavioral polish and mock loops
- ·Rehearse every story out loud. Cut to 2-3 minutes each
- ·Run 2 full mock loops with a senior 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: the loop is rooting for you to raise the bar, not to fail
See also
Other guides you'll want
FAQ
Common questions
- How much does a LinkedIn Senior Data Engineer make?
- Total compensation for LinkedIn Senior Data Engineer ranges $190K–$235K base • $330K–$460K total. Ranges shift by team and negotiation.
- How is the Senior Data Engineer loop different from other levels at LinkedIn?
- Senior Data Engineer loops run the same stages as other levels, but interviewers calibrate difficulty to independent technical leadership and cross-team influence, especially around independent system design and cross-team influence.
- How long should I prepare for the LinkedIn Senior Data Engineer interview?
- 8-10 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.