Meta Data Engineer Interview GuideMeta's loop is unusually SQL- and Python-heavy and famously time-boxed: the technical screen packs several problems into each half with an explicit pass bar, and data modeling is run as a dedicated round rather than embedded in design or SQL the way most FAANG peers do it. Algorithm difficulty skews easy-to-medium and data-contextualized, so the differentiator is speed and clean communication, not exotic data structures.
First contact to offer, stage by stage, with how long each round runs and roughly when it lands.
The loop stage by stage, from recruiter call to offer.
Non-technical call covering background, motivation for joining Meta, and role fit. The recruiter checks whether experience aligns with team and level. They will ask about scale: how much data you have worked with, what tools you used, why Meta specifically.
Live SQL coding, usually 1 to 2 problems. Meta phone screens lean on aggregation, window functions, and multi-step queries set in Meta-like contexts: user engagement, ad impressions, content moderation. The interviewer watches your problem-solving process as much as your final answer.
Harder than the phone screen. 2 to 3 SQL problems with increasing complexity. The first is a warm-up (basic aggregation). The second involves window functions or multi-step logic. The third may involve optimization: the query works, now discuss how to make it efficient at scale.
Design a data model for a Meta product: Facebook Events, Instagram Stories, Marketplace, or Messenger. Define fact and dimension tables, grain, slowly changing dimensions, and how the model supports specific analytical queries. This round checks whether you think about data as a system.
Design a data pipeline at Meta scale. Examples: real-time ad metrics, content moderation event processing, cross-platform activity aggregation. The interviewer cares about reasoning at scale (billions of events per day), fault tolerance, data quality, and batch vs streaming tradeoffs.
Meta calls this the 'values' round. Questions focus on collaboration, conflict resolution, and impact. They want specific STAR-format examples. Meta values 'Move Fast' and 'Build Social Value,' so frame stories around speed of delivery and user impact.
Our prediction of the question mix by domain and difficulty for this company's data engineer loop, from live listings and interview reports.
The domain and difficulty mix we predict for a Meta data engineer loop, across 14 problems. It updates as more Meta data lands.
Reported by candidates from real loops, tagged by domain, round, level, and year. Expand for what the round is scoring.
Given a star schema with sales transactions and a book dimensions table, identify all authors who have published at least 5 books.
Given a star schema with sales transactions and customer registration data, calculate the percentage of total sales where the sale was completed on the same day the customer registered.
Given sales transactions data, find customers who purchased 3 or more books on both the first day of sales recorded in the dataset AND the last day of sales recorded.
Write a function invert_dict(d) that takes a dictionary where all values are hashable (strings, ints, tuples, etc.) and returns a new dictionary mapping each original value to a sorted list of all keys that mapped to it. Example: d = {'a': 1, 'b': 2, 'c': 1, 'd': 3, 'e': 2} Output: {1: ['a', 'c'], 2: ['b', 'e'], 3: ['d']} d = {'x': 'hello', 'y': 'hello', 'z': 'world'} Output: {'hello': ['x', 'y'], 'world': ['z']} d = {} Output: {} Edge cases: empty dict, all keys mapping to the same value, values that are already unique (each list has one element).
Write a function hourly_event_counts(logs) where each log is a tuple of (timestamp_str, event_type). Timestamps are in 'YYYY-MM-DD HH:MM:SS' format. Return a dictionary where keys are hour strings ('YYYY-MM-DD HH') and values are dictionaries of {event_type: count}. Example: logs = [ ('2024-03-15 09:12:00', 'click'), ('2024-03-15 09:45:00', 'click'), ('2024-03-15 09:30:00', 'view'), ('2024-03-15 10:05:00', 'click'), ] Output: { '2024-03-15 09': {'click': 2, 'view': 1}, '2024-03-15 10': {'click': 1} } logs = [] Output: {} Edge cases: empty log list, logs spanning multiple days, single event…
Write a function top_n_per_category(records, n) where each record is a dictionary with 'category' and 'value' keys. Return a dictionary mapping each category to a list of its top n records sorted by value descending. If two records in the same category have equal values, preserve their original relative order. Example: records = [ {'category': 'A', 'value': 10}, {'category': 'A', 'value': 30}, {'category': 'B', 'value': 20}, {'category': 'A', 'value': 20}, {'category': 'B', 'value': 50}, {'category': 'B', 'value': 40}, ] top_n_per_category(records, 2) Output: { 'A': [{'category': 'A'…
Design a relational database schema for a ride-sharing app. Must specify tables (users, drivers, rides, payments, etc.), field types for each column, primary keys, and foreign key relationships. Discuss one-to-many (driver to rides) and many-to-many (riders to ride requests) relationships, normalization choices, and indexing strategy.
Problem Solving round: interviewer sent problem statement 24 hours in advance. FB Connectivity product collects data from cellular towers and marries them to FB app logs to create a data product. Candidate must design: data access strategies, big data processing system components, and the Data Warehouse model. Candidate used Excalidraw to draw architecture. Evaluated on thoroughness of schema design, storage strategy, and system component choices. Virginia USA, 9+ YOE.
Here is my Meta Data Engineer Phone Screen Interview experience (1 hour total- 30 mnts for SQL and 30 Mnts for Python)--Failed 1) I have been asked what section you want to code and i requested for SQL Tables names are like. Books, Author, Sales, Cutomer Book table contains Book name, Author ID, Genre a) Write a query to print author ID who wrote 5 or more Genres ---Test cases passed ''' select author_id from book group by author_id having count(author_id) >=5'' b) Sale table contains ID, tranaction_date, customer ID customer table contains Id, registered_date Write query to find sales…
Company: Facebook | Meta Position: Data Engineer Location: Virginia, USA Interview: Virtual Onsite YoE: 9+ yrs **Phone Interview**: There was one 1 hr phone interview, there was two section: Algo and SQL SQL: First I choose to go for SQL, So, my suggestion here: go for the section which you feel more confident, as it will help to boost your confidence and also if you can solve first section little earlier then for second section you can buy some time. it was having 3 sql question from a given dataset, start with easier to little advance SQL.
How hard candidates rated the loop and how they felt, summarized across the reports below.
12 rated reports
Candidate accounts of the loop, each with its date, level, difficulty, and outcome. Scroll the feed.
12 candidate interview reports
real candidate submissions
Hello, I have a 60 minute phone screen coming up in 2 weeks for Data Engineer, Product Analytics role at Meta. Can someone please share info if they have gone through tech screen recently? I was told by the recruiter that there will be 5 SQL questions and 5 coding questions. Thank you
Hi All, I have a meta data engineering loop round interview comin up in next few weeks. I have been told there will be 3 full stach data engineering rounds, i.e.: Product Sense, data modeling, SQL, Python. Recruiter made it very clear Leetcode / hacker ranks type of questions won't help.
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Can fellow leetcoders share some tips / prep material on how to prep for the upcoming interviews. I am interested especially in product sense and python type of questions.
Hi, I have an upcoming technical screen for Meta Data Engineer, New Grad role, does anyone know how to prepare for the python part? Its 5 questions sql and 5 python
Hi Everyone. I recetly appeared for the Data Engineer Interview for Meta. I have created a detailed video here: Even after doing well in the interviews from my end, I wasn't able to make it. But I hope my experience can help others to prepare. I will try to post in detail questions in another post. Thanks.
I'm preparing for Meta’s 5/5 Python-SQL phone screening. Has Meta updated their interview pattern for screening? Also, regarding the code portion, are we expected to execute the code or just dry run it?
Greetings, I would appreciate it if someone from the community could help me prepare for my upcoming data engineering onsite interview for Product Analytics. Could you please provide insights on the following: 1. For the coding portion, should I expect Python code related to ETL tasks or more challenging LeetCode-style problems?
2. According to the practice guide shared by Meta, the Python coding standards seem quite high, comparable to what's expected for a core SDE role. Is this accurate? 3. Is the Python code in the onsite interview generally more complex than what was asked in the phone screen?
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4. In the phone screen, the SQL problems were based on a single schema. Do they use a similar structure for the onsite interview as well? I would greatly appreciate any guidance you can provide to help me prepare effectively for this interview. Thank you in advance for your assistance. ------------------- Update - Completed the Onsite Interview Round 1 - Ownership - All about Why, When, Situation related to past experience.
Example - Tell me a time when you had a conflict and took a data-driven decision, etc. Round 2, 3, 4 - Coding/Technical: 1.
Could someone please share some insights on the Data Engineering new format for (full-stack) Python coding questions used in Meta's virtual onsite rounds?
Hi all, I went through a DE onsite with Meta (unsure about level) and apparently the new format for these are considered "full stack DE" vs the previous ETL1, ETL2, Data modeling structure. Besides the behavioral interview, the 3 technicals all had some element of: * data modeling * product sense * data visualization - in one session I was asked to sketch the visualizations in a tool * SQL heavy - requested to write ANSI sql, even though the phone screen was PostgreSQL * 1 Python question at the end ~LC medium level, nothing I've seen on LC to mirror this exactly Has anyone else gone through this style of onsite?
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I am wondering what the weights are for each of the sections and if each of the sections have their own individual hire / no-hire evaluations. Thanks in advance for your thoughts.
Hello All, I have an up coming Interview in Meta for Data Engineer in Product Analytics. Can any one please help me with the interview preparation and what is expected in the Interview.. Thanks in advance
Hi All, I have a technical phone interview scheduled for Data Engineer role at Meta in upcoming 2 weeks. It is supposed to be **5+5 (python & sql) coderpad interview**. If anyone in the recent past or anytime has interviewed for a similar role, I would really appreciate any interview questions, suggestions, tips, questions or any interview experience that you could share it with me. It will be really helpful for me to prepare for the interview!
Has anyone recently appeared for Meta Data Engineer Phone Interview. They said I can expect 5 SQl and 5 DSA questions. Does anyone know what kind of question I can expect. Please help! Edit: It been more than 10 days since I had my 1st round and I didn't receive any response yet.
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Is it normal? I have sent a follow up email couple of days back still no response. I have checked career site as well, there's no change in the staus. Anyone knows how long should I wait for the results
hey i have a data engineering internship - Analytics interview coming up. already had the phone screen now I'm in the 1st Coding interview round. I've been doing top facebook questions mostly easy/medium. Please suggest any helpful resources and specific previously asked questions or any advice!!
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Anyone who is currently intervieweing for the same role and had their interview already- any help would be greatly appreciated. <3
Find every user active on 3 or more CONSECUTIVE days. This gaps-and-islands shape shows up in nearly every DE SQL round. Edit the query and run it against the seed data.
The problems our model expects in this company's interview, grouped by round. Work the shapes that come up, not the ones that read well on a list.
The patterns that sink otherwise strong candidates here.
Because peers embed it, candidates often treat modeling as an afterthought. Meta gives it a dedicated round. Not defining the grain, skipping SCD reasoning, or jumping to a schema before the business question sinks otherwise-strong candidates.
A design that would work for millions of rows but never addresses billions of events, partitioning, or backpressure reads as junior. Every system design answer should name the scale it operates at and how it holds up there.
In ranking questions (top-N per group with a minimum-volume threshold), applying the volume filter after ROW_NUMBER produces wrong results. The filter must come before the window. Interviewers watch for this specifically.
Meta's SQL deep dive is a shared document and the interviewer follows your thinking through your code. Working silently, using cryptic CTE names (au instead of active_users), or not narrating trade-offs costs signal even when the final query is correct.
'I communicated with the team' does not move the needle in the values round. Meta wants specific STAR stories with quantified impact. Vague answers in a tiebreaker round cost offers.
Tactical advice for the dimensions this company weighs.
When designing a pipeline, mention billions of events. When writing SQL, discuss performance on tables with hundreds of billions of rows. When proposing optimization, name the partition strategy. Scale awareness is the single biggest differentiator.
Practice with tables named user_sessions, ad_impressions, content_interactions, and friend_requests. Think about what data each Meta feature generates; every like, comment, share, impression, and scroll is tracked. Your SQL fluency on Meta-shaped data will be visible.
Meta is metrics-driven. DEs support A/B testing, metric computation, and experiment analysis. Mention how your pipeline supports experimentation: control vs treatment, metric slicing by variant, and statistical power for small-effect detection.
Some candidates over-prepare for technical and under-prepare for behavioral, but at Meta the values round can be a tiebreaker. Prepare specific stories demonstrating cross-team collaboration and shipping under deadlines. Generic answers cost offers.
Meta's SQL deep dive uses a shared document with no autocomplete. Type deliberately, name CTEs descriptively (active_users not au), and comment any non-obvious logic. The interviewer follows your thinking through your code structure.
The signals behind the questions. Shape every answer around these.
Most FAANG peers embed data modeling inside a system design or SQL round. Meta runs it as its own 45-minute round: design fact and dimension tables for a real product, define the grain explicitly, and reason about slowly changing dimensions. It is the single biggest structural differentiator of the loop.
Meta operates at exabyte scale. When designing a pipeline, the expectation is billions of events per day; when writing SQL, performance on tables with hundreds of billions of rows; when optimizing, a named partition strategy. Scale awareness is what separates strong candidates from passing ones.
Meta is metrics-driven, and DEs support A/B testing, metric computation, and experiment analysis. The strongest answers connect a pipeline back to experimentation: control vs treatment, metric slicing by variant, and statistical power for small-effect detection.
Meta's behavioral round is framed around 'Move Fast' and 'Build Social Value,' and it carries real weight; it can decide a close loop. Candidates who over-index on technical prep and under-prepare specific, quantified collaboration stories lose offers here.
The roles behind this loop. Prep against the levels and locations they are actually filling.
Design, build, and launch collections of sophisticated data models and visualizations that support use cases across different products or domains
Design, build, and launch collections of sophisticated data models and visualizations that support multiple use cases across different products or domains
Design and scale the data pipelines and instrumentation that capture agent telemetry, usage signals, and outcome metrics across a fragmented and fast-moving tool landscape
Candidates should also have a proven track record of leading and scaling efforts related to end-to-end analytics systems, operational skills to drive efficiency and speed, project management leadership, and a vision for how data can proactively improve companies.
In this role, you will collaborate with software engineering, data science, and product management teams to design/build scalable data solutions across Meta to optimize growth, strategy, and user experience for our 3 billion plus users, as well as our internal employee community.
The numbers, tech stack, and team structure live on the company overview.
Level-specific pages: the comp, the bar, and what the loop tests at each seniority.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
How the role, pay, and loop stack up against peer companies.
Reading a solution is not the same as writing one
Every engineer who has frozen on a query they had read a dozen times knows the gap. The only preparation that closes it is producing the answer yourself, under time, before the interview does it for you
76% of hiring managers reject on the coding task, not the resume
From HackerRank's 2024 Developer Skills Report. Candidates who look strong on paper still fail the live screen if they haven't done timed, executable practice
5 problem shapes cover 80% of data engineer loops
Dedup, sessionization, top-N-per-group, slowly-changing dimensions, partition tricks. Writing the shapes by hand turns the unfamiliar into pattern recognition