meta-data-engineer

Meta Data Engineer Interview: A Complete Guide

Tim S

Tim S

Tim currently works at FAANG as a data analyst. He's been in the industry for 5+ years and writes for Big Tech Interviews on the weekends. His favorite language is SQL and calls Denver, CO home.

Securing a role as a data engineer at Meta, one of the world’s leading technology companies, is a significant career milestone. 

Meta’s data engineers are instrumental in building and optimizing the vast data infrastructure that supports the company’s diverse products and services. 

As a candidate, navigating the rigorous interview process is critical to demonstrating your technical expertise and alignment with Meta’s innovative culture. 

This data engineer interview guide aims to equip you with everything you need to succeed in your Meta data engineer interview, including tips on how to excel in the Meta data engineer coding interview. We will cover the essential skills and qualifications required for the role, typical interview questions, and proven preparation strategies. 

Thus, by the end of this guide, you’ll have a comprehensive understanding of how to present your technical abilities and problem-solving skills and how you fit into the dynamic environment at Meta.

Table of Contents

Understanding the Meta Data Engineer Role 

At Meta, data engineers are the backbone of the company’s data infrastructure. They are responsible for creating and maintaining the systems that allow data to flow seamlessly across the organization. 

This role involves a mix of designing, implementing, and optimizing large-scale data pipelines and storage solutions. Data engineers at Meta ensure that the data is accessible, reliable, and secure, enabling teams to make data-driven decisions that drive the company forward. 

Key Responsibilities and Skills Required 

As part of the Meta data engineer interview preparation, here is a list of the key responsibilities and skills required: 

Key Responsibilities: 

The Meta data engineer’s key responsibilities include: 

  1. Data Pipeline Development: Designing and developing efficient—and effective—data pipelines to ingest and process massive data volumes from multiple sources. 
  1. Data Quality Management: Implementing data validation and cleansing processes to ensure the accuracy and consistency of data. 
  1. ETL/ELT  Processes: Performing Extract, Transform Load (ETL) or Extract, Load, Transform (ELT) operations to convert raw data into structured formats suitable for analysis.
  1. Scalable Data Storage Solutions: Developing and maintaining scalable data storage solutions such as data warehouses and lakes. 
  1. Data Security: Ensuring the secure handling and storage of data to protect it from data breaches and unauthorized access. 
  1. Performance Optimization: Continuously monitoring and optimizing data systems for performance and scalability. 
  1. Collaboration: Working closely with data scientists, analysts, and other stakeholders to understand their data requirements. 

Skills Required: 

Some of the most critical skills required include the following: 

  1. Programming Languages: Proficiency in Python, YAML, or Scala is essential for developing data pipelines and processing frameworks.
  1. Big Data Technologies: Experience with tools such as Hadoop, Spark, and Kafka for processing and managing massive datasets. 
  1. Database Management: Knowledge of standard relational databases such as PostgreSQL and MySQL, columnar databases such as ClickHouse, vector databases such as Pinecone and Weaviate, and non-relational databases such as MongoDB and Cassandra, among others. 
  1. ETL Tools: Familiarity with ETL tools such as Talend, Apache Airflow, or Informatica for automating data pipeline processes. 
  1. Cloud Platforms: Knowledge and understanding of cloud computing platforms—AWS, Google Cloud Platform, and Microsoft Azure. 
  1. Analytical Skills: Strong problem-solving abilities and analytical thinking to design efficient data solutions. 
  1. Communication Skills: Ability to explain complex technical concepts to non-technical stakeholders and collaborate effectively with cross-functional teams. 

Difference Between Meta Data Engineer and Other Data Engineering Roles 

While the core responsibilities of the data engineering role at Meta are similar to those in other organizations such as the Amazon Data Engineer Interview, there are unique aspects to the role at Meta:

  1. Scale and Complexity: Meta operates at a massive scale, dealing with petabytes of data daily; therefore, Meta’s data engineers must design systems that can handle these huge data volumes efficiently and reliably. 
  1. Cutting-Edge Technologies: Meta employs the latest and most innovative technologies in its data infrastructure. Thus, data engineers must remain current with emerging tools and technologies to improve data processing capabilities continually. 
  1. Cross-Functional Collaboration: Given Meta’s diverse range of applications and services, data engineers frequently collaborate with different teams, including product development, machine learning, and business analytics, to meet their data requirements and support strategic initiatives. 
  1. Focus on User Privacy: With a strong emphasis on user data privacy and security, data engineers at Meta play a key role in implementing robust data protection measures to comply with regulatory requirements and build user trust. 

Meta Data Engineer Interview Process and Stages 

Landing a data engineer role at Meta involves navigating a comprehensive and rigorous interview process to evaluate technical expertise and cultural fit. 

Meta’s interview stages are structured to identify candidates with the right mix of technical skills, problem-solving abilities, and alignment with the company’s core values. Understanding each stage of the process can help you prepare effectively and increase your chances of success. 

Here is an outline of the main stages of the Meta Data Engineer interview process: 

  1. Initial Screening 

This interview process begins with an initial screening conducted by a recruiter. 

During this 10- to 20-minute conversation, the recruiter will focus on understanding your professional background, motivations for applying to Meta, and career aspirations. It is also an opportunity for you to walk through your resume, emphasizing your experiences and skills that align with the requirements of this role, including the following details: 

  1. Be prepared to discuss your previous projects, particularly those that showcase your proficiency in data engineering. 
  1. Highlight your technical skills, such as your experience with programming languages such as Python, YAML, Scala, or Java, and your familiarity with big data technologies such as Hadoop, Kafka, and Spark. 
  1. The recruiter will also assess your cultural fit with Meta, so be sure to convey your enthusiasm for the company and how you see yourself contributing to its mission and values. 

2. Technical Assessment 

Following the initial screening, you will undergo a technical phone screening, a crucial stage of the Meta data engineer coding interview process where your technical abilities are assessed. 

This stage is crucial for showcasing your technical abilities. The 45-minute interview typically involves a deep dive into your resume, followed by coding questions conducted through an online collaborative coding editor. 

The questions will test your knowledge of computer science fundamentals, including algorithms, data structures, and SQL queries from basic to advanced queries such as: 

You have the following tables:

Table: Orders
+----------+---------+-----------+-------------+--------+
| order_id | sale_id | client_id | city        | amount |
+----------+---------+-----------+-------------+--------+
| 1        | 1       | 101       | Los Angeles | 1000.00|
| 2        | 2       | 102       | New York    | 1500.50|
| 3        | 3       | 103       | Chicago     | 2000.00|
| 4        | 4       | 104       | New York    | 2500.75|
| 5        | NULL    | NULL      | Los Angeles | 3000.00|
+----------+---------+-----------+-------------+--------+

And

Table: Clients
+-----------+--------------+-------------+
| client_id | client_name  | city        |
+-----------+--------------+-------------+
| 101       | John Doe     | New York    |
| 102       | Jane Smith   | New York    |
| 103       | Jim Beam     | Los Angeles |
| 104       | Jill Jackson | Los Angeles |
| 105       | Jack Johnson | Chicago     |
+-----------+--------------+-------------+

Suppose we need to extract all orders along with their corresponding client information. This includes clients who haven’t placed any orders yet, but we are only interested in customers who have placed orders, irrespective of the value of the order. How will you write the SQL query? 

The explanation is as follows: 

  1. SELECT Clause: Select relevant columns from both Orders (aliased as o) and Clients (aliased as c).
  2. FROM Clause: The main table is Orders.
  3. LEFT JOIN Clause: Perform a LEFT JOIN with the Clients table on client_id to include all orders and their corresponding client information.
  4. WHERE Clause: Filter to include only those orders where client_id is not NULL, ensuring we only get customers who have placed orders.
  5. ORDER BY Clause: Sort the results by order_id.

This query ensures that we retrieve all orders along with their corresponding client information, focusing only on customers who have placed orders. The LEFT JOIN allows us to include client information for each order, and the WHERE clause ensures we exclude any orders without a client ID.

Therefore, it is vital to articulate your thought process clearly and demonstrate confidence in your coding skills. 

3. Onsite Interview

The onsite interview is the most intensive part of the process and usually consists of 3-5 sessions, each lasting about 45 minutes. 

For data engineering roles, expect to face 2 onsite interviews focused on coding and system design and a behavioral interview. 

The technical interviews will test your problem-solving skills and ability to design scalable and efficient data systems. The behavioral interview assesses your cultural fit and how well your values align with Meta’s. 

This is your chance to impress with your character and demonstrate you can thrive in Meta’s collaborative environment.

  1. Behavioral and Technical Components

As described above, Meta’s interview process includes both behavioral and technical components. 

The technical interviews will test your expertise in areas such as SQL, Python, YAML, SCALA, and data pipeline design. You should be prepared to solve complex problems, optimize database queries, and discuss your experience with big data technologies such as Spark and Hadoop. 

On the other hand, the behavioral interview will focus on how you handle team dynamics, conflict resolution, and project management. Using the STAR—Situation, Task, Action, Result—method to structure your responses can effectively convey your experiences and skills. 

Key Technical Topics and Example Questions

Preparing for a data engineering interview at Meta requires a thorough understanding of several interview topics, covering coding challenges, SQL proficiency, data modeling, and ETL processes. In this section, we’ll dive into each of these areas and provide example questions to help you understand what to expect during the interview.

  1. Coding Challenges

In the coding challenges that span software engineering, data science, and data engineering, the common problems and sample questions you can expect will test your knowledge in areas such as string manipulation, array handling, and other functional data structures. For instance: 

  • String Manipulation: Write a function that returns the number of times a given character occurs in a string. For example, given the input string “abracadabra” and the character “a”, the output should be 5. 
  • Array Handling: Given an array containing a list of integers, replace each 0 value with the product of all non-zero elements to the left of it. If there are no non-zero elements to the left, replace the 0 with 1. For example, the input array [2, 3, 0, 4, 0, 5] should be transformed to [2, 3, 6, 4, 24, 5].
  • Algorithmic Challenge: Given an array of integers, find the maximum difference between indices `j` and `i` such that `arr[j] < arr[i]`. For example, if the input array is `[90, 70, 50, 80, 20, 30, 10]`, the output will be `4` because `arr[6] < arr[0]` and `6 – 0 = 6`.
  1. SQL Proficiency

Use these tables to answer the following sample questions: 

Table: Orders
+----------+---------+-----------+-------------+--------+
| order_id | sale_id | client_id | city        | amount |
+----------+---------+-----------+-------------+--------+
| 1        | 1       | 101       | Los Angeles | 1000.00|
| 2        | 2       | 102       | New York    | 1500.50|
| 3        | 3       | 103       | Chicago     | 2000.00|
| 4        | 4       | 104       | New York    | 2500.75|
| 5        | NULL    | NULL      | Los Angeles | 3000.00|
+----------+---------+-----------+-------------+--------+

And

Table: Clients
+-----------+--------------+-------------+
| client_id | client_name  | city        |
+-----------+--------------+-------------+
| 101       | John Doe     | New York    |
| 102       | Jane Smith   | New York    |
| 103       | Jim Beam     | Los Angeles |
| 104       | Jill Jackson | Los Angeles |
| 105       | Jack Johnson | Chicago     |
+-----------+--------------+-------------+

SQL proficiency is critical for any data engineer, especially a Meta data engineer. You will be tested on your ability to write efficient queries using joins and indexes and to what extent you understand database design principles. For instance: 

  • Joins: 
sql-join-example

This question tests your understanding of different types of joins and their applications. Suppose we need to extract all orders along with their corresponding client information. This includes clients who haven’t placed any orders yet, but we are only interested in orders with amounts exceeding $2000. Order results by total amount ASC.

Here is the SQL query that returns clients with orders with amounts greater than $2000: 

SELECT 
    o.order_id,
    c.client_name
FROM 
    Orders o
LEFT JOIN 
    Clients c ON o.client_id = c.client_id
WHERE 
    o.amount > 2000
ORDER BY 
    o.amount ASC;
  • Indexing: Explain how indexing can improve query performance and give examples of scenarios where indexing would be beneficial.
  • Database Design: 

The first sample question is as follows: Given a large dataset with duplicates, write a query to import it into a data warehouse while optimizing for query speed. 

The script that imports the data contained within the dataset  into a data warehouse while optimizing for speed is as follows: 

-- Step 1: Create staging tables without importing data
CREATE TABLE staging_post_relationship (
    "date" DATE,
    "post_id" INTEGER,
    "relationship" VARCHAR(50),
    "interaction" VARCHAR(50)
);

CREATE TABLE staging_posts (
    "post_id" INTEGER,
    "poster_id" INTEGER
);

-- Step 2: Create the main data warehouse tables with partitioning and compression
CREATE TABLE post_relationship_dw (
    "date" DATE,
    "post_id" INTEGER,
    "relationship" VARCHAR(50),
    "interaction" VARCHAR(50)
)
PARTITION BY HASH(post_id) PARTITIONS 10;

CREATE TABLE posts_dw (
    "post_id" INTEGER PRIMARY KEY,
    "poster_id" INTEGER
)
PARTITION BY HASH(post_id) PARTITIONS 10;

-- Step 3: Create indexes to optimize query speed
CREATE INDEX idx_relationship_post_id ON post_relationship_dw(post_id);
CREATE INDEX idx_posts_poster_id ON posts_dw(poster_id);

-- Step 4: Optionally, create Parquet versions of the tables with compression
CREATE TABLE post_relationship_dw_parquet (
    "date" DATE,
    "post_id" INTEGER,
    "relationship" VARCHAR(50),
    "interaction" VARCHAR(50)
)
STORED AS PARQUET
WITH (COMPRESSION = 'SNAPPY');

CREATE TABLE posts_dw_parquet (
    "post_id" INTEGER,
    "poster_id" INTEGER
)
STORED AS PARQUET
WITH (COMPRESSION = 'SNAPPY');

-- Step 5: Insert deduplicated data into the main tables
INSERT INTO post_relationship_dw ("date", "post_id", "relationship", "interaction")
SELECT DISTINCT "date", "post_id", "relationship", "interaction"
FROM staging_post_relationship;

INSERT INTO posts_dw ("post_id", "poster_id")
SELECT DISTINCT "post_id", "poster_id"
FROM staging_posts;

-- Step 6: Insert data into the Parquet tables
INSERT INTO post_relationship_dw_parquet ("date", "post_id", "relationship", "interaction")
SELECT "date", "post_id", "relationship", "interaction"
FROM post_relationship_dw;

INSERT INTO posts_dw_parquet ("post_id", "poster_id")
SELECT "post_id", "poster_id"
FROM posts_dw;

A second question might involve designing a relational database schema for storing metadata about songs, including fields such as song title, artist, album, and release year. 

Here is the SQL script to create the tables in this relational database 

-- Artists Table
CREATE TABLE Artists (
    artist_id INT PRIMARY KEY AUTO_INCREMENT,
    artist_name VARCHAR(255) NOT NULL
);

-- Albums Table
CREATE TABLE Albums (
    album_id INT PRIMARY KEY AUTO_INCREMENT,
    album_name VARCHAR(255) NOT NULL,
    artist_id INT,
    release_year INT,
    FOREIGN KEY (artist_id) REFERENCES Artists(artist_id)
);

-- Songs Table
CREATE TABLE Songs (
    song_id INT PRIMARY KEY AUTO_INCREMENT,
    song_title VARCHAR(255) NOT NULL,
    artist_id INT,
    album_id INT,
    release_year INT,
    FOREIGN KEY (artist_id) REFERENCES Artists(artist_id),
    FOREIGN KEY (album_id) REFERENCES Albums(album_id)
);

-- Genres Table
CREATE TABLE Genres (
    genre_id INT PRIMARY KEY AUTO_INCREMENT,
    genre_name VARCHAR(255) NOT NULL
);

-- Song_Genres Table
CREATE TABLE Song_Genres (
    song_id INT,
    genre_id INT,
    PRIMARY KEY (song_id, genre_id),
    FOREIGN KEY (song_id) REFERENCES Songs(song_id),
    FOREIGN KEY (genre_id) REFERENCES Genres(genre_id)
);
  1. Data Modeling and ETL/ELT Processes 

Data modeling and ELT/ETL processes are integral to the role of a data engineer. In this section, you must demonstrate your ability to design efficient data models and ETL pipelines. For instance: 

  1. Data Modeling: Design a schema for a data warehouse that supports high-volume analytical processing. You must create tables that capture historical data for analysis, ensuring they can handle complex queries and large data volumes. 
  1. ETL/ELT Processes: Describe how you would build an ETL/ELT pipeline to move data from a transactional database to a data warehouse. This might include data extraction methods, transformation rules, and loading strategies. You could also be asked to optimize an existing ETL pipeline to improve performance and reliability. 

Preparation Strategies and Resources

To excel in a data engineer interview at Meta, it is critical to master vital technical skills, utilize effective preparation resources, and engage in mock interviews and networking. Here is a guide on the preparation strategies needed. 

Here is a list of resources that will help you prepare for your interview: 

  1. Books and Online Courses
  • Books:SQL for Data Scientists” by Renee M. P. Teate and “Python for Data Analysis” by Wes McKinney are both excellent resources.
  • Online Courses: Platforms such as Coursera offer comprehensive courses such as the “Meta Database Engineer Professional Certificate,” which covers SQL, Python, and data engineering principles​.
  1. Coding Platforms

Coding platforms such as Big Tech Interviews and Khan Academy provide numerous coding challenges frequently encountered in technical interviews, as well as practice problems and competitions that can help improve your coding skills in a timed environment. 


Mock Interviews and Networking Tips 

Conducting mock interviews and implementing the following networking tips are critical parts of your Meta data engineer interview preparation.

  1. Mock Interviews

It is imperative to practice mock interviews and simulate actual interviews regularly using platforms such as bigtechinterviews.com to practice with real interview questions and get feedback from peers. Moreover, it is important to practice coding on a whiteboard or use coding environments such as Coderpad to simulate the actual interview. 

  1. Networking Tips
  1. Join Professional Communities: Engage with professional associations and online forums such as LinkedIn groups focused on data engineering. 
  1. Attend Industry Events: Participate in data science and engineering meetups, webinars, and conferences to build connections. 
  1. Seek Referrals: Leverage your network to get referrals from current or former Meta employees, which can significantly boost your chances of getting noticed by recruiters. 

Focusing on these core skills, utilizing the recommended resources, and actively engaging in mock interviews and networking can help you effectively prepare for your data engineer interview at Meta and increase your chances of success. 

Behavioral and Product Sense Questions 

The last part of your interview includes the following behavioral and product sense questions: 

Typical Behavioral Questions

These questions are designed to assess how well you handle different workplace scenarios, your decision-making process, and how effectively you work in a team. Some of the common types of questions you might encounter include: 

  1. Handling Conflict
  • Example Question: Tell me about a time you had a disagreement with a team member. How did you resolve it? 
  • How to Answer: Use the STAR—Situation, Task, Action, Result—method to describe a specific instance where you faced conflict, how you approached resolving it, and the outcome. Emphasize your communication skills and ability to find a compromise. 
  1. Decision-Making
  • Example Question: Can you give an example of a difficult decision you had to make at work?  
  • How to Answer: Describe the situation, the options you considered, the reasoning behind your choice, and the results of your decision. Highlight your analytical skills and ability to weigh the pros and cons. 
  1. Teamwork
  • Example Question: Describe a time when you worked with a team to achieve a goal. 
  • How to Answer:  Focus on a specific project or task where collaboration was key. Explain your role, how you contributed to the team’s success, and what the team achieved. Demonstrate your ability to work well with others and contribute to a common goal.​

Product Sense Questions 

Meta places a significant emphasis on product sense, especially in roles that involve data-driven decision-making. These questions evaluate your ability to understand and improve products through data insights. Here are some typical product sense questions: 

  1. Understanding Data-Driven Products 
  • Example Question: How would you use data to improve Facebook’s Newsfeed? 
  • How to Answer:  Discuss how you would analyze user engagement data, identify trends or issues, and suggest changes based on your findings. Mention specific metrics you would track, such as click-through rates or user retention, and how you would use A/B testing to validate your recommendations.
  1. Improving Products 
  • Example Question: Imagine you are the product manager for Facebook Messenger. Daily active users have decreased significantly. How would you approach identifying the problem and fixing it?
  • How to Answer:  Outline a systematic approach to diagnosing the issue, such as analyzing user data to spot drop-off points or conducting user surveys to gather feedback. Propose actionable strategies to address the problem, such as introducing new features or improving existing ones, and explain how you would measure the success of these changes.​

Preparing for these types of questions involves understanding Meta’s products and their impact and demonstrating your ability to leverage data to drive product decisions. Practice with real-world scenarios and focus on clearly and concisely communicating your thought process and solutions. 

FAQs

If you are considering a role as a data engineer at Meta, you may have several questions about what to expect in terms of salary, preparation, and critical skills. Here are some of the most frequently asked questions to help you prepare for your interview and understand more about the role.

  1. What is the average salary for a Meta data engineer? 

The average salary for a data engineer at Meta is around $164,113 per annum. This includes base salary and additional compensation such as bonuses and stock options. However, total compensation can vary significantly depending on experience and location. 

  1. How long should you prepare for a Meta data engineer interview? 

Preparation time can vary, but it is recommended that you spend at least 2-3 months preparing for this interview. This includes practicing coding problems, brushing up on SQL and Python skills, and understanding data modeling and ELT/ETL pipelines or processes. 

  1. What are the key skills needed for a Meta data engineer? 

The key skills for a Meta data engineer include: 

  • SQL: Proficiency in querying, joins, indexing, and database design. 
  • Python: Strong understanding of data structures, functions, and data processing libraries such as Pandas and NumPy.
  • Data Processing: Knowledge of batch processing with Hadoop/Spark and real-time processing with Kafka.
  • Data Modeling: Ability to design and optimize data schemas. 
  1. How do you approach a coding challenge in an interview?

When approaching a coding challenge, you: 

  • Understand the Problem: Read the problem statement carefully and identify the inputs and expected outputs. 
  • Plan Your Solution: Outline the logic and steps needed to solve the problem before coding.
  • Write Clean Code: Use appropriate data structures to implement your solution clearly and concisely. 
  • Test Your Code: Run test cases to ensure your solution works correctly and handles edge cases. 
  • Explain Your Thought Process: Communicate your approach and reasoning to the interviewer as you code. 
  1. What are the best resources for SQL preparation? 

The best resources for SQL preparation include: 

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