Meta Senior Data Engineer Interview (IC5)
Meta (IC5) Senior Data Engineer loop: SQL-heavy with fast-paced coding expectations and a product-sense orientation. Bar at this level: independent technical leadership and cross-team influence. Typical 5-8 years of data engineering experience.
Compensation
$215K–$260K base • $420K–$620K total (IC5)
Loop duration
4.8 hours onsite
Rounds
6 rounds
Location
Menlo Park, NYC, Seattle, London, remote for select teams
Compensation
Meta Senior Data Engineer total comp
Offer-report aggregate, 2026. Level mapped: L5. Typical experience: 8-10 years (median 9).
25th percentile
$246K
Median total comp
$341K
75th percentile
$353K
Median base salary
$190K
Median annual equity
$100K
Practice problems
Meta senior data engineer practice set
Problems the Meta senior data engineer loop tends to ask, surfaced from signals in current job descriptions. Click any to start practicing.
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.
Two Wallets
We run an online education marketplace with two user types: students paying for access, and instructors paying for listing and promotion features. Both can subscribe to different plan tiers using multiple saved payment methods. Design the data model, then write the SQL for month-over-month subscription growth.
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.
Walk into Meta knowing the system design pattern they'll test.
Rolling 7-day active users
Count distinct users active in the trailing 7 days for each date. Product analytics staple.
The Identity Problem
Old systems. New demands. The same customer appears under three different names.
Pulled from debriefs where system design separated levels.
The loop
How the interview actually runs
01Recruiter screen
30 minNon-technical. The recruiter confirms level, product area (Ads, Integrity, Instagram, Reality Labs), and motivations. How you describe past work signals IC3/IC4/IC5.
- →Quantify everything: row counts, daily event volumes, TB processed
- →Research the specific team. Meta has dozens of DE teams with different tech stacks
- →Ask whether the loop includes a Python round; some teams do, some don't
02Technical phone screen
45 minLive SQL coding, 1-2 problems, in a shared doc with no syntax highlighting. Problems emphasize window functions, multi-step logic, and event-stream schemas.
- →Think out loud from the start, silence worries the interviewer
- →Expect window functions: ROW_NUMBER, LAG, LEAD, running totals
- →Ask clarifying questions: NULL handling, duplicates, timezone of timestamps
03Onsite: SQL deep-dive
45 min2-3 SQL problems with increasing complexity. The last often adds an optimization discussion: 'Your solution works, now make it efficient on 500B rows.'
- →Practice writing SQL without autocomplete. Meta uses a shared doc
- →When discussing optimization, mention partition pruning, predicate pushdown
- →Use CTEs to break complex queries into readable steps
04Onsite: Python / data manipulation
45 minPractical data work, not LeetCode. Parse JSON logs, transform nested structures, write a data validation function, build a small ETL step.
- →Practice file I/O, dictionary manipulation, list comprehensions
- →Write helper functions instead of one monolithic block
- →Handle edge cases explicitly, empty inputs, missing keys, malformed data
05System 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'
Level bar
What Meta 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.'
Meta-specific emphasis
Meta's loop is characterized by: SQL-heavy with fast-paced coding expectations and a product-sense orientation. Calibrate your preparation to that, generic FAANG prep will not close the gap on company-specific expectations.
Behavioral
How Meta frames behavioral rounds
Move Fast
Meta's culture rewards shipping and iterating. Stories about shipping a V1, measuring, and iterating land harder than stories about getting a design perfect before launch.
Focus on Long-Term Impact
Paired with Move Fast. Meta wants DEs who ship fast without creating 3-year tech debt. Balance matters.
Build Awesome Things
Meta wants people who care deeply about craft. Your ETL pipeline is not just a job, it is a thing you built.
Live in the Future
Senior and above: betting on the technology curve. Candidates who talk about where data infrastructure is going in 3 years land strongly.
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, Meta weights this round heavily
- ·Read Meta'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+ Meta-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 Meta'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 Meta 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
Related pages on Meta's loop
FAQ
Common questions
- What level is Senior Data Engineer at Meta?
- On Meta's ladder, Senior Data Engineer sits at IC5. Expectations center on independent technical leadership and cross-team influence.
- How much does a Meta Senior Data Engineer make?
- Across 11 offer samples from 2026, Meta Senior Data Engineer total compensation lands at $246K (P25), $341K (median), and $353K (P75), median base $190K and median annual equity $100K. Typical experience range: 8-10 years..
- How is the Senior Data Engineer loop different from other levels at Meta?
- Round structure is shared across levels; what changes is what each round tests. For Senior Data Engineer the emphasis is independent technical leadership and cross-team influence, with particular attention to independent system design and cross-team influence.
- How long should I prepare for the Meta Senior Data Engineer interview?
- 8-10 weeks of focused prep is typical for candidates already working as a DE. Less than 4 weeks is tight; the behavioral story bank usually takes longer than candidates expect.
- Does Meta interview data engineers differently than software engineers?
- Yes. DE loops at Meta weight SQL heavier, include pipeline/system-design rounds tuned to data workloads, and probe for production data experience (ingestion patterns, data quality, backfill) that generalist SWE loops skip.