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 make?

Meta Junior Data Engineer offers span $160K-$171K across 4 samples from 2026, with a median of $166K, median base $130K and median annual equity $21K. Typical experience range: 2-3 years..

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

Meta Junior Data Engineer Interview (IC3)

Q: 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.

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.

Compensation

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

Loop duration

3.8 hours onsite

Rounds

5 rounds

Location

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

Compensation

Meta Junior Data Engineer total comp

Across 4 samples

Offer-report aggregate, 2026. Level mapped: L3. Typical experience: 2-3 years (median 3).

25th percentile

$160K

Median total comp

$166K

75th percentile

$171K

Median base salary

$130K

Median annual equity

$21K

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.

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→

Pythonmedium~10 min

Detect Cycle in Sequence

You are given a list of integers where each value at index i is the next index to visit (or -1 to terminate). Starting from index 0, follow the chain and return True if you revisit any index, False otherwise. Out-of-range indices (including -1) count as termination, not a cycle.

Open in practice environment→

Modelingeasy~15 min

The Balance Always Reconciles

We're a consumer lending company that offers personal loans, auto loans, and mortgages. Customers make monthly payments, but sometimes they pay early, miss payments, or refinance. The operations team needs outstanding balances and the risk team needs to flag delinquent accounts. Can you design the schema?

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→

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

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 Data Engineer→Meta Senior Data Engineer→Meta Staff Data Engineer→Meta Principal Data Engineer→Amazon Junior Data Engineer→Google Junior Data Engineer→Microsoft Junior Data Engineer→Apple Junior Data Engineer→Netflix Junior Data Engineer→Airbnb Junior Data Engineer→Meta Data Engineer Salary→Meta Interview Guide→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 make?: Meta Junior Data Engineer offers span $160K-$171K across 4 samples from 2026, with a median of $166K, median base $130K and median annual equity $21K. Typical experience range: 2-3 years..
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.

Interview Prep

Data Engineer Interview Prep Guide→

50+ guides covering every round, company, and role

Interview Rounds

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Question Sets

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