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 make?

Looking at 8 sampled offers from 2026, Meta Staff Data Engineer total comp comes in at $471K median, ranging from $415K to $509K, median base $242K and median annual equity $186K. Typical experience range: 10-12 years..

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

Meta Staff Data Engineer Interview (IC6)

Q: 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.

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.

Compensation

$255K–$310K base • $600K–$950K total (IC6)

Loop duration

4.8 hours onsite

Rounds

6 rounds

Location

Menlo Park, NYC, Seattle, London, remote for select teams

Compensation

Meta Staff Data Engineer total comp

Across 8 samples

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

25th percentile

$415K

Median total comp

$471K

75th percentile

$509K

Median base salary

$242K

Median annual equity

$186K

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.

SQLeasy~5 min

Full Customer Order List

Return first_name, last_name, and country for every customer in customers. Sort alphabetically by first_name, then last_name.

Open in practice environment→

Pythoneasy~10 min

The Repeat Offenders

Given a list, return the values that appear more than once, each listed only once, in the order of their first appearance in the input.

Open in practice environment→

SQLeasy~5 min

High Volume Batch Jobs

Surface all batch jobs that processed more than 5000 rows, showing each job's name, priority, and rows processed, ranked from most to fewest.

Open in practice environment→

Pythoneasy~10 min

The Word Inventory

Given a list of words, return a dict with two keys. 'counts' maps each word to its frequency. 'unique' is the sorted list of words that appear exactly once.

Open in practice environment→

Prepare for the interview

01 / Open invite

02min.

Walk into Meta knowing the system design pattern they'll test.

a Meta system design query, the same shape a screen would give you.

The diff against expected. Where ties broke. What you missed.

sandbox

1source → bronze → silver → gold

2 ingest : CDC + Kafka

3 transform : dbt + Airflow

4 serve : Snowflake

Execute your solution0.4s avg.

MetaInterview question

Solve a Meta problem

Rolling 7-day active users

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

	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 AS d
	INNER JOIN activity AS 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

Prepare for the interview

03 / From the bank03 of many

03hand-picked.

Two Million Boxes by Monday Morning

Hard35 min

Shipped, maybe. Delivered, debatable.

Open the sandbox

Pick your level

MetaL3Disappearing Ink SalesforceL4All at Once AdobeL5Thirty Cities, One Forecast

Pulled from debriefs where system design separated levels.

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 Junior Data Engineer→Meta Data Engineer→Meta Senior Data Engineer→Meta Principal Data Engineer→Amazon Staff Data Engineer→Google Staff Data Engineer→Microsoft Staff Data Engineer→Apple Staff Data Engineer→Netflix Staff Data Engineer→Airbnb Staff Data Engineer→Meta Data Engineer Salary→Meta Interview Guide→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 make?: Looking at 8 sampled offers from 2026, Meta Staff Data Engineer total comp comes in at $471K median, ranging from $415K to $509K, median base $242K and median annual equity $186K. Typical experience range: 10-12 years..
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.

Interview Prep

Data Engineer Interview Prep Guide→

50+ guides covering every round, company, and role

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Question Sets

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