Meta

Data Engineer Interview Experience

July 11, 2024

Positive ExperienceNo Offer

Process

My interview process consisted of the following steps:

Online application
Recruiter call
Technical screening (virtual). I was unfortunately rejected after this round and did not proceed to the onsite interviews.
Virtual onsite

Questions

Python and SQL questions.

I wasn't fast enough, so I ran out of time.

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Interview Statistics

The following metrics were computed from 347 interview experiences for the Meta Data Engineer role in United States.

Success Rate

15%

Pass Rate

Meta's interview process for their Data Engineer roles in the United States is extremely selective, failing the vast majority of engineers.

Experience Rating

Positive63%

Neutral24%

Negative13%

Candidates reported having very good feelings for Meta's Data Engineer interview process in United States.

Meta Work Experiences

Good pay, but expect yourself as a small gear in a big engine

Data Engineer • Current Employee

Menlo Park, California, United States

Pros: Good pay. Excellent resume eye-catcher. Best practices in data engineering and some good documentation that can be learned from.

Cons: You are a small moving part of a big engine. Leadership doesn't know you exist as an IC. Try hard enough to prove your impact to get into the cult. Too much empire building. Job specific - even though you would learn a lot about Data Engineering, the learning you get exponentially decreases. You learn more how to communicate rather than technical stuffs.

Good pay, but expect yourself as a small gear in a big engine

Data Engineer • Current Employee

Menlo Park, California, United States

Pros: Good pay. Excellent resume eye-catcher. Best practices in data engineering and some good documentation that can be learned from.

Cons: You are a small moving part of a big engine. Leadership doesn't know you exist as an IC. Try hard enough to prove your impact to get into the cult. Too much empire building. Job specific - even though you would learn a lot about Data Engineering, the learning you get exponentially decreases. You learn more how to communicate rather than technical stuffs.

Good Pay, High Stress

Data Engineer • Former Employee

New York, New York, United States

Pros: Good pay, PTO, learned a lot.

Cons: Random firings, depending on team work can be very stressful

Good Pay, High Stress

Data Engineer • Former Employee

New York, New York, United States

Pros: Good pay, PTO, learned a lot.

Cons: Random firings, depending on team work can be very stressful

2.5 years - best company I was working in so far

Data Engineer • Current Employee

London, England, United Kingdom

Pros: 1. Impact-driven culture! Not the amount of work matters, but the amount of impact by this work. 2. The company is 99.9% data-driven. All conflicts are solved with data. It's really easy to prove your point and convince others. 3. People. They are open, critical-thinking, and just amazing! Every single person, starting from my teammates and finishing with Zuck. 4. Open culture (no decisions are hidden by leadership). 5. Feedback culture - you can improve every day! People provide you feedback all the time.

Cons: 1. Sometimes it is a bit tricky to manage work/life balance. There are so many fantastic projects that you can't stop but work on. 2. The company is really big. It's hard to see the full picture.

2.5 years - best company I was working in so far

Data Engineer • Current Employee

London, England, United Kingdom

Pros: 1. Impact-driven culture! Not the amount of work matters, but the amount of impact by this work. 2. The company is 99.9% data-driven. All conflicts are solved with data. It's really easy to prove your point and convince others. 3. People. They are open, critical-thinking, and just amazing! Every single person, starting from my teammates and finishing with Zuck. 4. Open culture (no decisions are hidden by leadership). 5. Feedback culture - you can improve every day! People provide you feedback all the time.

Cons: 1. Sometimes it is a bit tricky to manage work/life balance. There are so many fantastic projects that you can't stop but work on. 2. The company is really big. It's hard to see the full picture.

Good pay, but expect yourself as a small gear in a big engine

Data Engineer • Current Employee

Menlo Park, California, United States

Pros: Good pay. Excellent resume eye-catcher. Best practices in data engineering and some good documentation that can be learned from.

Cons: You are a small moving part of a big engine. Leadership doesn't know you exist as an IC. Try hard enough to prove your impact to get into the cult. Too much empire building. Job specific - even though you would learn a lot about Data Engineering, the learning you get exponentially decreases. You learn more how to communicate rather than technical stuffs.

4.0
Good pay, but expect yourself as a small gear in a big engine
Data Engineer • Current Employee
Menlo Park, California, United States
April 8, 2023
Pros: Good pay. Excellent resume eye-catcher. Best practices in data engineering and some good documentation that can be learned from.
Cons: You are a small moving part of a big engine. Leadership doesn't know you exist as an IC. Try hard enough to prove your impact to get into the cult. Too much empire building. Job specific - even though you would learn a lot about Data Engineering, the learning you get exponentially decreases. You learn more how to communicate rather than technical stuffs.

Good Pay, High Stress

Data Engineer • Former Employee

New York, New York, United States

Pros: Good pay, PTO, learned a lot.

Cons: Random firings, depending on team work can be very stressful

3.0
Good Pay, High Stress
Data Engineer • Former Employee
New York, New York, United States
March 4, 2025
Pros: Good pay, PTO, learned a lot.
Cons: Random firings, depending on team work can be very stressful

2.5 years - best company I was working in so far

Data Engineer • Current Employee

London, England, United Kingdom

Pros: 1. Impact-driven culture! Not the amount of work matters, but the amount of impact by this work. 2. The company is 99.9% data-driven. All conflicts are solved with data. It's really easy to prove your point and convince others. 3. People. They are open, critical-thinking, and just amazing! Every single person, starting from my teammates and finishing with Zuck. 4. Open culture (no decisions are hidden by leadership). 5. Feedback culture - you can improve every day! People provide you feedback all the time.

Cons: 1. Sometimes it is a bit tricky to manage work/life balance. There are so many fantastic projects that you can't stop but work on. 2. The company is really big. It's hard to see the full picture.

5.0
2.5 years - best company I was working in so far
Data Engineer • Current Employee
London, England, United Kingdom
May 25, 2020
Pros: 1. Impact-driven culture! Not the amount of work matters, but the amount of impact by this work. 2. The company is 99.9% data-driven. All conflicts are solved with data. It's really easy to prove your point and convince others. 3. People. They are open, critical-thinking, and just amazing! Every single person, starting from my teammates and finishing with Zuck. 4. Open culture (no decisions are hidden by leadership). 5. Feedback culture - you can improve every day! People provide you feedback all the time.
Cons: 1. Sometimes it is a bit tricky to manage work/life balance. There are so many fantastic projects that you can't stop but work on. 2. The company is really big. It's hard to see the full picture.

Meta Interview Questions

Minimum Remove to Make Valid Parentheses

Given a string with parentheses, determine the minimum number of parentheses you must remove to make the string valid. A valid string has balanced and properly nested parentheses.

StringsStacksTwo Pointers

Valid Word Abbreviation

Determine if a given abbreviation is valid for a given word. An abbreviation can contain digits representing the number of skipped characters.

StringsTwo Pointers

Binary Tree Vertical Order Traversal

Traverse a binary tree and return a list of node values for each vertical line, ordered from left to right. Nodes on the same vertical line should be ordered from top to bottom.

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