This time Meta Data Engineer overall rhythm of the interview was very "Meta-flavored": the structure was clear, the logic was product + analysis hybrid, and the understanding of indicators, implementation capabilities, data abstraction, and coding style were all very serious. We were accompanied and coached throughout the entire process, and the overall experience was very smooth.
5-minute self-introduction: The focus is “product thinking + data implementation”
Meta's DE (Data Engineer) emphasizes more than the traditional ETL/Data Pipeline engineering position Product sense + indicator understanding ability.
The students’ introduction focused on three points:
- How have you broken down business problems into data problems in the past?
- What pipelines, monitoring and quality systems have you used?
- How to align indicator logic with PM/DS
The more concise this part is, the better. Meta pays more attention to “the ability to think about indicators together.”
Product Sense: In-depth discussion around "Effective Reading"
This round was particularly exciting. The interviewer gave a light scenario and asked:
What do you think is the definition of "effective reading"? How to measure? Why measure it like this?
What we prepare students to do in reverse is:
1. Let’s start with user value – why should we define it?
- The platform needs to determine whether the user really "consumes content"
- To provide signals to Ranking/Feed/advertising systems
- Calculate engagement and payout for Creator
2. Then provide a clearly structured indicator framework
Four key elements:
- Reading duration (duration)
- Screen coverage %
- reading continuity or session
- Interactive behaviors as weighted items (such as stay, slide back, click away)
Meta especially likes to hear:
"We should use Do users really look into it? to define effective reading rather than a single exposure or brief dwell. "
3. Demonstrate trade-off
for example:
- High screen-to-body ratio but short reading → not necessarily effective
- It takes a long time but the screen-to-body ratio is very low → you may not watch it
Giving one or two counterexamples will show that you really understand.
The interviewer was very satisfied with the whole thought process and jumped right into SQL.
SQL: Core high-frequency question "Effective reading post calculation"
The table structure given in the interview is relatively simplified, roughly as follows
event_log(post_id, user_id, duration_seconds, max_screen_coverage)
Require:
- find out effective reading Posts that satisfy:
- total watch duration > X seconds
- Maximum screen-to-body ratio > Y%
- Reading the same post multiple times requires polymerization duration and max_coverage.
The structure of the golden answer we compiled is as follows:
Breakdown of key points
- First group by aggregation duration + screen coverage
- duration using SUM()
- Coverage using MAX()
- Finally, use HAVING to filter
- If there is session division, you need to first create a window function or customize the session id.
Typical SQL solutions (general version)
SELECT
post_id,
SUM(duration_seconds) AS total_duration,
MAX(max_screen_coverage) AS max_coverage
FROM event_log
GROUP BY post_id
HAVING
SUM(duration_seconds) > X
AND MAX(max_screen_coverage) > Y;
What the interviewer wants to focus on is:
- Can you form the correct one in 30 seconds? Aggregation model
- Can you explain "why coverage takes max() instead of avg()"
We have done training on similar question types in advance, so we can answer them very stably.
Python: Convert SQL to Streaming processing (highlight question)
Meta likes to take the “streaming pipeline thinking” test very much.
The interview requires you to:
Given the same event stream as a SQL table, turn it into a "real-time processing version"
And can identify sessions and calculate effective reading.
Here is the structural framework we teach during tutoring:
(1) First abstract the data structure
Each event:
{
"post_id": ...,
"duration": ...,
"screen_coverage": ...,
"timestamp": ...
}
(2) Maintain a session state machine for each user/post
Test points:
- Determine session end (timeout)
- cumulative duration
- record max screen coverage
- Output "valid reading results" at the end of the session
(3) Standard answer ideas
Typical code structure
state = {} # key = (user_id, post_id)
for event in stream:
key = (event.user_id, event.post_id)
if key not in state:
state[key] = {
"total_duration": 0,
"max_coverage": 0,
"last_ts": event.timestamp
}
session = state[key]
# If session timeout is exceeded, output and reset
if event.timestamp - session["last_ts"] > SESSION_GAP:
output_if_valid(session, key)
reset(session)
# Update status
session["total_duration"] += event.duration
session["max_coverage"] = max(session["max_coverage"], event.screen_coverage)
session["last_ts"] = event.timestamp
# Flush again after the stream ends
for key, session in state.items():
output_if_valid(session, key)
The interviews focus most on:
- Do you have the concept of session?
- Is the "max coverage" logic maintained correctly?
- Can you explain why streaming restore SQL logic
The students scored almost full marks on this question.
Last 5 minutes: BQ + reverse questions
Regular Meta BQ:
- Tell me about a cross-team collaboration challenge
- How do you handle ambiguous requirements
- What’s one time you improved data quality
We asked students to answer using Meta’s favorite “IC owner” style:
Emphasis on metrics, emphasis on impact, emphasis on what you're driving.
Ask two advanced questions in reverse:
- How teams assess the impact of DE
- DE’s depth of cooperation with DS/ML Infra
The interviewer liked it very much and nodded along the way.
ProgramHelp Auxiliary instructions
What we provide for this interview is full coaching + VO real-time auxiliary prompts.
In the three modules of Product Sense, SQL aggregation logic, and Python session streaming,
We have done question type prediction + core framework training in advance.
My performance on the day of the interview was extremely stable, without any pitfalls.What we serve is "real interview logic", not memorizing question banks.
What you should present to the interviewer is always a candidate who is thoughtful, structured, and capable of landing.
