What level is Staff Data Engineer at Meta?

At Meta, Staff Data Engineer corresponds to the IC6 level. The bar emphasizes organizational impact beyond a single team and tech strategy ownership without people-management responsibilities.

How much does a Meta Staff Data Engineer in Seattle make?

Looking at 17 sampled offers from 2023-2026, Meta Staff Data Engineer in Seattle total comp comes in at $504K median, ranging from $454K to $750K, median base $244K and median annual equity $250K. Typical experience range: 10-13 years..

Does Meta actually hire data engineers in Seattle?

Yes, Meta maintains a Seattle office and hires Staff 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 Staff Data Engineer interview?

Most working DEs find 10-12 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.

Interview Guide · 2026

Meta Staff Data Engineer Interview in Seattle (IC6)

Meta (IC6) Staff Data Engineer loop: SQL-heavy with fast-paced coding expectations and a product-sense orientation. Bar at this level: organizational impact beyond a single team and tech strategy ownership. Typical 8-12 years of data engineering experience. Details on the Seattle office (Seattle / Bellevue, WA) follow, including compensation calibrated to the local market.

Compensation

$235K–$285K base • $552K–$874K total (IC6)

Loop duration

4.8 hours onsite

Rounds

6 rounds

Location

Seattle / Bellevue, WA

Compensation

Meta Staff Data Engineer in Seattle total comp

Across 17 samples

Offer-report aggregate, 2023-2026. Level mapped: L6. Typical experience: 10-13 years (median 12).

25th percentile

$454K

Median total comp

$504K

75th percentile

$750K

Median base salary

$244K

Median annual equity

$250K

Practice problems

Meta staff data engineer practice set

4 problems

Interview problems predicted for Meta staff data engineers based on their actual job descriptions. Click any problem to work it in a live coding environment.

Modelingmedium~30 min

The Retail Tables That Need a New Home

You are given an existing transactional database from a retail operation covering orders, customers, products, stores, and employees. The analytics team cannot write performant queries against this structure. Redesign it as a dimensional warehouse that supports reporting on sales performance, product mix, and customer behavior.

Open in practice environment→

Architecturehard~30 min

Viewing Event Pipeline

We need to track what our subscribers are watching. This data feeds everything from our recommendation models to operations dashboards that monitor playback quality in real time. Design a data pipeline for our viewing events.

Open in practice environment→

Modelingmedium~30 min

The Talent Funnel

A job marketplace tracks candidate activity from the moment a job listing is viewed through to an accepted offer. The analytics team needs to measure funnel drop-off rates at each stage, compare conversion by job type and location, track time-to-hire by company, and attribute sourcing channel credit. Design a schema that supports all of this.

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→

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.

Seattle / Bellevue, WA

Meta in Seattle

No state income tax. AWS and Azure anchor the DE market, with dense mid-to-senior hiring across Amazon, Microsoft, and their ecosystem.

Seattle comp lands about 8% below the reference band in line with local market rates. The Seattle office's interview loop mirrors the global loop structure; team assignment and comp-band negotiation are the main local variables.

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

05Architecture strategy

60 min

At staff level, system design expands to multi-system strategy: 'Design the data platform for a 500-person org' or 'We have 40 pipelines producing inconsistent output; how do you fix it?' The evaluator watches for whether you think about developer experience, tech-debt paydown, and multi-quarter roadmaps.

→Talk about teams and processes, not just technology
→Name the specific mechanisms you would create (code review standards, shared libraries, data contracts)
→Be ready to defend why not to build something you would build at senior level

Level bar

What Meta expects at Staff Data Engineer

Technical strategy ownership

Staff DEs set technical direction for multiple teams. Interviewers ask 'What tech decisions have you influenced across your org?' and probe depth: how did you socialize it, who pushed back, what trade-offs did you accept?

Multi-system design

Staff-level design is not one pipeline; it is the platform that 10 pipelines run on. Think data contracts, metadata stores, standardized ingestion patterns, shared orchestration, and the tradeoffs between standardization and team autonomy.

Tech-debt and migration leadership

Stories about leading a multi-quarter migration: the plan, the phasing, the stakeholder management, the rollback criteria. Staff DEs are expected to have shipped at least one such effort.

Mentorship scale

At staff, mentorship goes beyond 1:1 coaching: you have influenced hiring rubrics, run tech talks, or built onboarding that accelerated new hires.

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

Platform-level system design

·Design 3-5 multi-system platforms: metadata store, shared ingestion, governance layer
·Prepare 2-3 stories where you drove technical direction across teams
·Practice mock interviews with another staff+ engineer
·Review Meta's publicly described platform work for recent architectural shifts

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 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

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: the loop is rooting for you to raise the bar, not to fail

Adjacent guides to check

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

FAQ

Common questions

What level is Staff Data Engineer at Meta?: At Meta, Staff Data Engineer corresponds to the IC6 level. The bar emphasizes organizational impact beyond a single team and tech strategy ownership without people-management responsibilities.
How much does a Meta Staff Data Engineer in Seattle make?: Looking at 17 sampled offers from 2023-2026, Meta Staff Data Engineer in Seattle total comp comes in at $504K median, ranging from $454K to $750K, median base $244K and median annual equity $250K. Typical experience range: 10-13 years..
Does Meta actually hire data engineers in Seattle?: Yes, Meta maintains a Seattle office and hires Staff Data Engineer data engineers there. Team assignment may be office-locked or global; confirm with the recruiter before the loop.
How is the Staff Data Engineer loop different from other levels at Meta?: The format of the loop matches other levels; difficulty and evaluation shift to organizational impact beyond a single team and tech strategy ownership, and questions at this level dig into multi-team technical strategy and platform thinking.
How long should I prepare for the Meta Staff Data Engineer interview?: Most working DEs find 10-12 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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Published 2026-04-20Updated 2026-04-20