Meta Data Engineer Interview (IC4)
Meta's Data Engineer loop ((IC4) short) emphasizes SQL-heavy with fast-paced coding expectations and a product-sense orientation. 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
$175K–$215K base • $300K–$420K total (IC4)
Loop duration
3.8 hours onsite
Rounds
5 rounds
Location
Menlo Park, NYC, Seattle, London, remote for select teams
Compensation
Meta Data Engineer total comp
Offer-report aggregate, 2019-2026. Level mapped: L4. Typical experience: 4-7 years (median 6).
25th percentile
$225K
Median total comp
$258K
75th percentile
$306K
Median base salary
$173K
Median annual equity
$60K
Median total comp by year
Practice problems
Meta data engineer practice set
Practice problems tagged with Meta, the company each one was written for, at the data engineer level where we have them. Click any problem to work it in a live coding environment.
Bookends of the Week
A payments team is profiling how each week's spending clusters around its two edges, Monday and Sunday. For every week, give the percentage of that week's transaction volume that fell on Monday and the percentage that fell on Sunday, each rounded to the nearest whole number, earliest week first.
The Throttle Ceiling
A public API throttles each client to at most `max_requests` calls within any `window_seconds`-long stretch, and a call is accepted only when fewer than `max_requests` already-accepted calls fall inside the window ending at its arrival; a throttled call is turned away and never counts against later ones. Given `calls`, the arrival timestamps in arrival order, return a parallel list of booleans that is True for each accepted call and False for each throttled one.
Content Engagement Data Model
We run a large social content platform where creators publish posts (text, image, video) and people engage through views, reactions, comments, and shares. Each view carries how long the person watched and each reaction carries its kind (like, love, angry), while a comment can reply to another comment so reply threads nest. A share turns an existing post into a brand-new post of its own, so the model has to trace every reshare back to both the post it came from and the person who reshared it. Design a schema that powers virality, creator-performance, and feed-ranking dashboards, knowing that raw engagement runs to billions of events a day, far too much to scan on every dashboard load.
Both Ways
We're reconciling daily net revenue for an online marketplace, where a positive amount is a purchase and a negative amount is a refund. Include every purchase dated between January 1 and April 30, plus every refund no matter when it happened, and give each day's net revenue.
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.
Two Million Boxes by Monday Morning
Shipped, maybe. Delivered, debatable.
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
Level bar
What Meta expects at IC4 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.
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 your approach aloud. Interviewers weigh how you explain it, not only what you write
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 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: interviewers want to find reasons to hire you, not to reject you
See also
Adjacent guides to check
FAQ
Common questions
- What level is Data Engineer at Meta?
- At Meta, Data Engineer corresponds to the IC4 level. The bar emphasizes shipped production pipelines end-to-end and can debug them when they break without people-management responsibilities.
- How much does a Meta Data Engineer make?
- Looking at 306 sampled offers from 2019-2026, Meta Data Engineer total comp comes in at $258K median, ranging from $225K to $306K, median base $173K and median annual equity $60K. Typical experience range: 4-7 years..
- How is the Data Engineer loop different from other levels at Meta?
- The format of the loop matches other levels; difficulty and evaluation shift to shipped production pipelines end-to-end and can debug them when they break, and questions at this level dig into production pipeline ownership and on-call debugging.
- How long should I prepare for the Meta Data Engineer interview?
- Most working DEs find 6-8 weeks is about right. The technical prep scales with experience; the behavioral story bank is where candidates underestimate time.
- Does Meta interview data engineers differently than software engineers?
- Yes, the DE track at Meta emphasizes SQL depth, warehouse and pipeline design, and real production data experience (late data, backfills, quality checks), which generalist SWE loops don't test.
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