Interview Guide · 2026

Meta Senior Data Engineer Interview in Seattle (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. Below we dig into how this runs out of the Seattle office (Seattle / Bellevue, WA), with cost-of-living-adjusted compensation.

Compensation

$198K–$239K base • $386K–$570K total (IC5)

Loop duration

4.8 hours onsite

Rounds

6 rounds

Location

Seattle / Bellevue, WA

Compensation

Meta Senior Data Engineer in Seattle total comp

Across 41 samples

Offer-report aggregate, 2021-2026. Level mapped: L5. Typical experience: 7-11 years (median 9).

25th percentile

$286K

Median total comp

$366K

75th percentile

$413K

Median base salary

$204K

Median annual equity

$120K

Median total comp by year

2021
$300K n=3
2023
$284K n=5
2024
$299K n=4
2025
$404K n=8
2026
$390K n=19

Practice problems

Meta senior data engineer practice set

4 problems

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

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→
Architecturemedium~20 min

Real-Time POS Ingestion into Snowflake

Our retail stores run point-of-sale terminals that generate transactions all day. The business intelligence team currently gets a nightly batch of sales data but they want same-day visibility. We also have years of historical sales in our Snowflake warehouse that needs to stay consistent with whatever we build. Design a pipeline to bring POS data into Snowflake in near-real-time.

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

Employee Transfer Tracking System

We're a large tech company with 80,000 employees across 30 offices. People transfer between departments, change managers, and relocate to different offices. HR currently stores everything in a single employee table and loses history every time someone moves. Can you design a schema that tracks the full movement history?

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
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.

Meta pays about 8% less in Seattle than its reference band; this maps to local market compensation norms. The interview loop itself is identical to Meta's global process in Seattle; 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

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
01

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
02

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
03

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
04

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
05

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

See also

Related pages on Meta's loop

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

FAQ

Common questions

What level is Senior Data Engineer at Meta?
On Meta's ladder, Senior Data Engineer sits at IC5. Expectations center on independent technical leadership and cross-team influence.
How much does a Meta Senior Data Engineer in Seattle make?
Across 41 offer samples from 2021-2026, Meta Senior Data Engineer in Seattle total compensation lands at $286K (P25), $366K (median), and $413K (P75), median base $204K and median annual equity $120K. Typical experience range: 7-11 years..
Does Meta actually hire data engineers in Seattle?
Yes, Meta maintains a Seattle 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?
Round structure is shared across levels; what changes is what each round tests. For Senior Data Engineer the emphasis is independent technical leadership and cross-team influence, with particular attention to independent system design and cross-team influence.
How long should I prepare for the Meta Senior Data Engineer interview?
8-10 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.

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