What level is Junior Data Engineer at Meta?

Meta uses IC3 to designate Junior Data Engineers; this is an IC-track level focused on foundational SQL fluency and a willingness to learn production systems.

How much does a Meta Junior Data Engineer in New York make?

Meta Junior Data Engineer in New York offers span $140K-$164K across 9 samples from 2022-2026, with a median of $160K, median base $128K and median annual equity $20K. Typical experience range: 1-4 years..

Does Meta actually hire data engineers in New York?

Yes, Meta maintains a New York office and hires Junior 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 Junior Data Engineer interview?

6-8 weeks is the standard window for a working DE. Less than 4 weeks almost always means cutting the behavioral prep short.

Does Meta interview data engineers differently than software engineers?

The tracks diverge. DE at Meta weights SQL and pipeline-design rounds, and interviewers expect specific production data experience that SWE loops don't probe.

Interview Guide · 2026

Meta Junior Data Engineer Interview in New York (IC3)

Meta (IC3) Junior Data Engineer loop: SQL-heavy with fast-paced coding expectations and a product-sense orientation. Bar at this level: foundational SQL fluency and a willingness to learn production systems. Typical 0-2 years of data engineering experience. The New York, NY office has its own hiring cadence; the page below adjusts comp bands accordingly.

Compensation

$135K–$175K base • $195K–$265K total (IC3)

Loop duration

3.8 hours onsite

Rounds

5 rounds

Location

New York, NY

Compensation

Meta Junior Data Engineer in New York total comp

Across 9 samples

Offer-report aggregate, 2022-2026. Level mapped: L3. Typical experience: 1-4 years (median 2).

25th percentile

$140K

Median total comp

$160K

75th percentile

$164K

Median base salary

$128K

Median annual equity

$20K

Practice problems

Meta junior data engineer practice set

4 problems

Practice sets surfaced for Meta junior data engineer candidates by the same model that reads their job postings. Each card opens a working coding 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→

Architecturehard~25 min

Pharma Data Ingestion Pipeline with Governance

We're a pharmaceutical company ingesting data from clinical trial systems, commercial sales databases, and patient support program feeds. The data governance team has mandated that every dataset entering the warehouse must have a documented data quality check, a lineage trace, and an access control policy before it goes live. Design the ingestion pipeline and governance framework.

Open in practice environment→

Modelingeasy~25 min

A Number for the Seller

A seller on our marketplace wants to see their total products listed and total revenue earned per day. Design the data model that supports this view, and choose between a dimensional model or an ER model. Justify your choice. Then write the SQL to produce the report.

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.

New York, NY

Meta in New York

Finance-adjacent DE work is common; fintech and trading firms compete with Big Tech on comp. Required comp range disclosures in NY job postings.

Meta's New York office hires at the company's reference compensation band. Loop structure in New York matches the global Meta process; what differs is team placement and the compensation range.

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 Junior Data Engineer

SQL foundations

Junior rounds weight SQL the heaviest. Expect multi-table joins, aggregations, window functions, and one harder query involving self-joins or recursive CTEs. You do not need to design systems at this level, but you do need SQL to be reflexive.

Learning orientation

Interviewers probe how you pick up new tools. A strong story about learning a new stack in a prior role (even an internship or side project) can outweigh gaps in production experience.

Basic pipeline awareness

You should know what ETL vs ELT means, what a data warehouse is, and why idempotency matters, even if you have not built a production pipeline yourself.

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 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
·Shore up data engineering foundations: SQL, Python, one warehouse (Snowflake/BigQuery/Redshift)

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

Other guides you'll want

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

FAQ

Common questions

What level is Junior Data Engineer at Meta?: Meta uses IC3 to designate Junior Data Engineers; this is an IC-track level focused on foundational SQL fluency and a willingness to learn production systems.
How much does a Meta Junior Data Engineer in New York make?: Meta Junior Data Engineer in New York offers span $140K-$164K across 9 samples from 2022-2026, with a median of $160K, median base $128K and median annual equity $20K. Typical experience range: 1-4 years..
Does Meta actually hire data engineers in New York?: Yes, Meta maintains a New York office and hires Junior Data Engineer data engineers there. Team assignment may be office-locked or global; confirm with the recruiter before the loop.
How is the Junior Data Engineer loop different from other levels at Meta?: Junior Data Engineer loops run the same stages as other levels, but interviewers calibrate difficulty to foundational SQL fluency and a willingness to learn production systems, especially around SQL fundamentals, learning orientation, and basic pipeline awareness.
How long should I prepare for the Meta Junior Data Engineer interview?: 6-8 weeks is the standard window for a working DE. Less than 4 weeks almost always means cutting the behavioral prep short.
Does Meta interview data engineers differently than software engineers?: The tracks diverge. DE at Meta weights SQL and pipeline-design rounds, and interviewers expect specific production data experience that SWE loops don't probe.

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