What level is Data Engineer at Meta?

Data Engineer maps to IC4 on Meta's engineering ladder. This is an individual contributor level; expectations focus on shipped production pipelines end-to-end and can debug them when they break.

How much does a Meta Data Engineer in San Francisco Bay Area make?

Based on 95 offer samples covering 2019-2026, Meta Data Engineer in San Francisco Bay Area sees $234K at the 25th percentile, $268K at the median, and $356K at the 75th percentile, median base $176K and median annual equity $70K. Typical experience range: 4-9 years..

Does Meta actually hire data engineers in San Francisco Bay Area?

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

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

Plan for 6-8 weeks of prep if you're already a working DE. Under 4 weeks rushes the behavioral prep, which takes the most time.

Does Meta interview data engineers differently than software engineers?

They differ meaningfully. Meta's DE loop has heavier SQL, replaces the general system-design with a data-specific one (pipelines, warehouse design), and expects production data ops experience.

Interview Guide · 2026

Meta Data Engineer Interview in San Francisco Bay Area (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. The San Francisco / South Bay, CA office has its own hiring cadence; the page below adjusts comp bands accordingly.

Compensation

$175K–$215K base • $300K–$420K total (IC4)

Loop duration

3.8 hours onsite

Rounds

5 rounds

Location

San Francisco / South Bay, CA

Compensation

Meta Data Engineer in San Francisco Bay Area total comp

Across 95 samples

Offer-report aggregate, 2019-2026. Level mapped: L4. Typical experience: 4-9 years (median 6).

25th percentile

$234K

Median total comp

$268K

75th percentile

$356K

Median base salary

$176K

Median annual equity

$70K

Median total comp by year

2020

$221K n=7

2021

$230K n=12

2022

$248K n=8

2023

$317K n=12

2024

$270K n=5

2025

$264K n=20

2026

$302K n=29

Practice problems

Meta data engineer practice set

4 problems

Meta data engineer practice set, mapped from predicted domain emphasis. Tap into any problem to work it in the live 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→

Architecturemedium~25 min

The Queue That Wouldn't Stop Growing

Your streaming video event pipeline shows consumer lag spiking from near-zero to over 500,000 messages within two hours. You need to diagnose whether the cause is a producer burst or a consumer slowdown, then design a monitoring and auto-remediation system that can detect, alert on, and automatically recover from future lag events.

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→

Modelingmedium~25 min

Employee Application Time Tracking

We need to track how much time employees spend in each application. HR wants daily summaries of time-per-employee-per-application, and wants to flag any employee spending more than 10 hours/day in a single application. Design the schema to capture this data.

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.

San Francisco / South Bay, CA

Meta in San Francisco Bay Area

The reference market for US tech comp. Highest base DE salaries in the US, highest cost of living, deepest senior-engineer hiring pool.

Offers in San Francisco Bay Area use the same reference compensation band; no local adjustment applies. San Francisco Bay Area candidates run the same loop as global peers; the differences show up in team assignment and local comp calibration.

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 interview guides

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

FAQ

Common questions

What level is Data Engineer at Meta?: Data Engineer maps to IC4 on Meta's engineering ladder. This is an individual contributor level; expectations focus on shipped production pipelines end-to-end and can debug them when they break.
How much does a Meta Data Engineer in San Francisco Bay Area make?: Based on 95 offer samples covering 2019-2026, Meta Data Engineer in San Francisco Bay Area sees $234K at the 25th percentile, $268K at the median, and $356K at the 75th percentile, median base $176K and median annual equity $70K. Typical experience range: 4-9 years..
Does Meta actually hire data engineers in San Francisco Bay Area?: Yes, Meta maintains a San Francisco Bay Area 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?: The rounds look similar, but the bar calibrates to seniority. Data Engineer is evaluated on shipped production pipelines end-to-end and can debug them when they break. Questions at this level probe production pipeline ownership and on-call debugging.
How long should I prepare for the Meta Data Engineer interview?: Plan for 6-8 weeks of prep if you're already a working DE. Under 4 weeks rushes the behavioral prep, which takes the most time.
Does Meta interview data engineers differently than software engineers?: They differ meaningfully. Meta's DE loop has heavier SQL, replaces the general system-design with a data-specific one (pipelines, warehouse design), and expects production data ops experience.

Continue your prep

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