What level is Senior Data Engineer at Meta?

At Meta, Senior Data Engineer corresponds to the IC5 level. The bar emphasizes independent technical leadership and cross-team influence without people-management responsibilities.

How much does a Meta Senior Data Engineer in Austin make?

Looking at 9 sampled offers from 2022-2026, Meta Senior Data Engineer in Austin total comp comes in at $360K median, ranging from $268K to $460K, median base $188K and median annual equity $130K. Typical experience range: 6-14 years..

Does Meta actually hire data engineers in Austin?

Yes, Meta maintains a Austin office and hires Senior 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 Senior Data Engineer interview?

Most working DEs find 8-10 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 Senior Data Engineer Interview in Austin (IC5)

Meta (IC5) Senior Data Engineer loop: SQL-heavy with fast-paced coding expectations and a product-sense orientation. Bar at this level: independent technical leadership and cross-team influence. Typical 5-8 years of data engineering experience. Details on the Austin office (Austin, TX) follow, including compensation calibrated to the local market.

Compensation

$183K–$221K base • $357K–$527K total (IC5)

Loop duration

4.8 hours onsite

Rounds

6 rounds

Location

Austin, TX

Compensation

Meta Senior Data Engineer in Austin total comp

Across 9 samples

Offer-report aggregate, 2022-2026. Level mapped: L5. Typical experience: 6-14 years (median 10).

25th percentile

$268K

Median total comp

$360K

75th percentile

$460K

Median base salary

$188K

Median annual equity

$130K

Practice problems

Meta senior data engineer practice set

4 problems

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

Modelingmedium~30 min

Food Truck Operations Data Model

We operate a fleet of food trucks. Each truck has a menu and moves between locations throughout the day. Customers order at a truck and pay. Operations wants to know: which items sell best at which locations? Which trucks are most profitable by day? Design the data model to answer these questions.

Open in practice environment→

Architecturemedium~25 min

4,500 Stores Before Sunrise

Every night, 4,500 stores each upload a CSV of current inventory to S3. The replenishment team needs clean, validated data in the warehouse by 7 AM. Some files arrive late, some are malformed, and re-runs have been producing duplicates. Design the pipeline.

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→

Modelingmedium~25 min

Livestream Analytics Schema

We're building the analytics backend for a livestream platform. Creators go live, viewers watch and interact through chat and gifts. We need to track everything for creator payouts, content recommendations, and engagement analytics. Can you 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.

Austin comp lands about 15% below the reference band in line with local market rates. The Austin 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

05System design (pipeline architecture)

60 min

Design a production pipeline end-to-end: ingestion, transformation, storage, consumers, SLAs, failure modes, backfill strategy, and cost trade-offs. At senior level, you drive the conversation without prompting. Expect follow-ups about scale, cross-team coordination, and operational load.

→Anchor on the SLA and data shape before diagramming
→Discuss idempotency, partitioning, and backfill explicitly
→Estimate cost: 'This pipeline will cost roughly $X/month at this volume'

Level bar

What Meta expects at Senior Data Engineer

Independent technical leadership

Senior DEs drive pipeline designs without engineering manager involvement. Interviewers probe whether you can decompose ambiguous requirements, make architecture trade-offs, and defend your choices under scrutiny.

Cross-team coordination

Senior scope regularly spans multiple teams. Expect scenarios about a downstream team missing an SLA because of a change you made, or negotiating a schema migration with the team that owns the upstream source.

Production operational rigor

Fluent in on-call, alerting, data quality checks, and incident response. Dive-deep stories at this level should include correlating a metric drop to a specific commit or a timezone bug or a subtle ordering issue, not 'I looked at the logs.'

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 system design

·Design 5 pipelines on paper: daily aggregation, clickstream, CDC, ML feature store, real-time alerting
·For each, write SLA, partition strategy, backfill plan, and cost estimate
·Practice with a friend, senior-level system design is 50% driving the conversation
·Review Meta's open-source and engineering blog for in-house patterns

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

FAQ

Common questions

What level is Senior Data Engineer at Meta?: At Meta, Senior Data Engineer corresponds to the IC5 level. The bar emphasizes independent technical leadership and cross-team influence without people-management responsibilities.
How much does a Meta Senior Data Engineer in Austin make?: Looking at 9 sampled offers from 2022-2026, Meta Senior Data Engineer in Austin total comp comes in at $360K median, ranging from $268K to $460K, median base $188K and median annual equity $130K. Typical experience range: 6-14 years..
Does Meta actually hire data engineers in Austin?: Yes, Meta maintains a Austin office and hires Senior Data Engineer data engineers there. Team assignment may be office-locked or global; confirm with the recruiter before the loop.
How is the Senior Data Engineer loop different from other levels at Meta?: The format of the loop matches other levels; difficulty and evaluation shift to independent technical leadership and cross-team influence, and questions at this level dig into independent system design and cross-team influence.
How long should I prepare for the Meta Senior Data Engineer interview?: Most working DEs find 8-10 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