What level is Data Engineer at Meta?

On Meta's ladder, Data Engineer sits at IC4. Expectations center on shipped production pipelines end-to-end and can debug them when they break.

How much does a Meta Data Engineer in Austin make?

Across 7 offer samples from 2023-2026, Meta Data Engineer in Austin total compensation lands at $201K (P25), $400K (median), and $942K (P75), median base $176K and median annual equity $188K. Typical experience range: 6-11 years..

Does Meta actually hire data engineers in Austin?

Yes, Meta maintains a Austin office and hires Data Engineer data engineers there. Team assignment may be office-locked or global; confirm with the recruiter before the loop.

How is the Data Engineer loop different from other levels at Meta?

Round structure is shared across levels; what changes is what each round tests. For Data Engineer the emphasis is shipped production pipelines end-to-end and can debug them when they break, with particular attention to production pipeline ownership and on-call debugging.

How long should I prepare for the Meta Data Engineer interview?

6-8 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.

Interview Guide · 2026

Meta Data Engineer Interview in Austin (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. Below we dig into how this runs out of the Austin office (Austin, TX), with cost-of-living-adjusted compensation.

Compensation

$149K–$183K base • $255K–$357K total (IC4)

Loop duration

3.8 hours onsite

Rounds

5 rounds

Location

Austin, TX

Compensation

Meta Data Engineer in Austin total comp

Across 7 samples

Offer-report aggregate, 2023-2026. Level mapped: L4. Typical experience: 6-11 years (median 10).

25th percentile

$201K

Median total comp

$400K

75th percentile

$942K

Median base salary

$176K

Median annual equity

$188K

Practice problems

Meta data engineer practice set

4 problems

Problems the Meta data engineer loop tends to ask, surfaced from signals in current job descriptions. Click any to start practicing.

Modelinghard~40 min

Marketplace Sales Warehouse

We run a two-sided marketplace where buyers and sellers transact. The analytics team needs a self-service warehouse to analyze GMV, conversion rates, and seller performance. There is no provided schema. You are expected to establish the entities, their relationships, and the dimensional model from scratch. Start by asking clarifying questions before designing anything.

Open in practice environment→

Architecturemedium~25 min

The Vendor Who Never Warns You

We receive monthly data files from an external vendor. The problem is that the file structure changes unpredictably; new columns appear, column names get renamed, and occasionally columns are dropped. The data feeds a set of analyst dashboards that must not break when the file format changes. Design the ingestion pipeline.

Open in practice environment→

Modelingeasy~20 min

Event Ticketing System Data Model

We run an IT helpdesk platform. Users submit support tickets, which are assigned to agents. Tickets go through multiple status changes before being resolved. SLA compliance is critical: P1 tickets must be resolved within 4 hours, P2 within 24 hours. Design the schema, and describe how you would load data from a JSON API feed into it.

Open in practice environment→

Modelingmedium~25 min

Housing Marketplace Analytics

We run a housing marketplace. Sellers list properties, buyers view listings and submit leads. We need to measure conversion rate from view to lead by location and property type. Design the data model.

Open in practice environment→

Try itRolling 7-day active users

Count distinct users active in the trailing 7 days for each date. Product analytics staple.

rolling_7dau.sql

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WITH dates AS (
  SELECT DISTINCT activity_date FROM activity
)
SELECT
  d.activity_date AS day,
  COUNT(DISTINCT a.user_id) AS rolling_7d_users
FROM dates d
JOIN activity a
  ON a.activity_date <= d.activity_date
  AND julianday(d.activity_date) - julianday(a.activity_date) < 7
GROUP BY d.activity_date
ORDER BY d.activity_date;
WITH dates AS (
  SELECT DISTINCT activity_date FROM activity
)
SELECT
  d.activity_date AS day,
  COUNT(DISTINCT a.user_id) AS rolling_7d_users
FROM dates d
JOIN activity a
  ON a.activity_date <= d.activity_date
  AND julianday(d.activity_date) - julianday(a.activity_date) < 7
GROUP BY d.activity_date
ORDER BY d.activity_date;

Click Run to execute. Edit the code above to experiment.

Austin, TX

Meta in Austin

No state income tax. Apple, Meta, Google, Oracle, and Tesla all have material engineering presence. Cheaper COL than coastal metros.

Meta pays about 15% less in Austin than its reference band; this maps to local market compensation norms. The interview loop itself is identical to Meta's global process in Austin; local variation shows up in team and compensation.

The loop

How the interview actually runs

01Recruiter screen

30 min

Non-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 min

Live 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 min

2-3 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 min

Practical 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 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.

“Tell me about a time you shipped something before it was ready.”

Focus on Long-Term Impact

Paired with Move Fast. Meta wants DEs who ship fast without creating 3-year tech debt. Balance matters.

“Describe a decision where you chose long-term quality over short-term velocity.”

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.

“What's a data system you've built that you're proud of?”

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.

“How do you expect data engineering to change in the next 3 years?”

Prep timeline

Week-by-week preparation plan

8-10 weeks out

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

6 weeks out

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

4 weeks out

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

2 weeks out

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

Week of

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

Related pages on Meta's loop

Meta Data Engineer (global guide)→Meta Junior Data Engineer in Austin→Meta Senior Data Engineer in Austin→Meta Staff Data Engineer in Austin→Meta Principal Data Engineer in Austin→Meta Data Engineer in San Francisco Bay Area→Meta Data Engineer in New York→Meta Data Engineer in Seattle→Meta Data Engineer Salary→Data Engineer Interview Prep (hub)→

FAQ

Common questions

What level is Data Engineer at Meta?: On Meta's ladder, Data Engineer sits at IC4. Expectations center on shipped production pipelines end-to-end and can debug them when they break.
How much does a Meta Data Engineer in Austin make?: Across 7 offer samples from 2023-2026, Meta Data Engineer in Austin total compensation lands at $201K (P25), $400K (median), and $942K (P75), median base $176K and median annual equity $188K. Typical experience range: 6-11 years..
Does Meta actually hire data engineers in Austin?: Yes, Meta maintains a Austin office and hires Data Engineer data engineers there. Team assignment may be office-locked or global; confirm with the recruiter before the loop.
How is the Data Engineer loop different from other levels at Meta?: Round structure is shared across levels; what changes is what each round tests. For Data Engineer the emphasis is shipped production pipelines end-to-end and can debug them when they break, with particular attention to production pipeline ownership and on-call debugging.
How long should I prepare for the Meta Data Engineer interview?: 6-8 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.

Continue your prep

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Published 2026-04-20Updated 2026-04-20