Data Engineer I applicants have rated the interview process at Meta with 3 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 100% positive. To compare, the company-average is 58.1% positive. This is according to Glassdoor user ratings.
Candidates applying for Data Engineer I roles take an average of 60 days to get hired, when considering 1 user submitted interviews for this role. To compare, the hiring process at Meta overall takes an average of 37 days.
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I applied online. I interviewed at Meta in Jul 2025
Interview
first round is the technical screening for 30 mins, 15 mins for sql and 15 mins for python containing 3 questions each.
After the qualification, loop round contains 3 technical rounds(starting with success metrics of a real world product(uber, reels, netflix..) , designing a data model with attributes and entities) followed by given 3 questions each on sql and python) of 1 hour with one behavioral round.
I applied through other source. I interviewed at Meta
Interview
HR Phone screening, checking fits for the position, normal and basic questions before continuing next stages. later she described the upcoming stages which requires a preparing of 3 weeks on avg
Interview questions [1]
Question 1
Describe me one project in your current comapny and how did you address it
I applied through an employee referral. I interviewed at Meta (Sunnyvale, CA) in Nov 2025
Interview
The SQL questions are typically medium difficulty, involving joins, aggregations, window functions, and CTEs, often tied to real business scenarios like retention or funnel analysis. The coding questions (mostly in Python) lean toward easy to medium difficulty and cover arrays, strings, hashmaps, and 2-pointer techniques. Focus your prep on Meta-tagged LeetCode and StrataScratch problems, especially in SQL and data-heavy logic, and practice writing clean, performant queries.
Interview questions [1]
Question 1
SQL Questions
Python Questions
Data Modeling:
facts:
* different types of facts and their grain
* pro/con of your fact design
* don't be afraid to give more than one fact table
* identify pk, fk and relationship (1 to 1, 1 to M)
* pay attention to the grain you defined
dimensions:
* give attributes
* bridge table, scd 2, role play, pro/con of each type in terms of performance