What is a Data Engineer at Meta IT?

At Meta, Data Engineering is not just a support function; it is a core pillar of the engineering organization that drives product strategy and operational efficiency. As a Data Engineer within Meta IT (often aligned with Enterprise Engineering or specific Infrastructure pillars), you are the architect of the data ecosystem that powers internal tools, business analytics, and the massive infrastructure supporting billions of users across Facebook, Instagram, WhatsApp, and Reality Labs.

You will work at an unprecedented scale. The role involves building and maintaining efficient, reliable, and scalable data pipelines that handle petabytes of data. You aren't just moving data from point A to point B; you are defining the data models that allow Data Scientists and Product Managers to make decisions that impact global connectivity. You will tackle complex challenges in distributed computing, data quality, and privacy, ensuring that the company’s internal data engine runs as smoothly as its consumer-facing products.

This position demands a blend of strong engineering principles and business acumen. You will be expected to "move fast" and take ownership of end-to-end data solutions. Whether you are optimizing Apache Spark jobs for efficiency or designing a star schema for a new internal metric, your work will directly influence how Meta understands its business and its users.

Common Interview Questions

These questions are based on real interview experiences from candidates who interviewed at this company. You can practice answering them interactively on Dataford to better prepare for your interview.

Getting Ready for Your Interviews

Preparing for a Data Engineering role at Meta requires a strategic approach. Do not expect a generic "ETL developer" interview; Meta evaluates candidates on their ability to think like software engineers who specialize in data. You must demonstrate that you can build systems that are not only functional but also scalable and fault-tolerant.

Your interview performance will be assessed against these core criteria:

SQL Proficiency and Data Manipulation This is the most critical technical filter. Interviewers expect you to write error-free, highly optimized SQL on a whiteboard or shared editor without an IDE. You must demonstrate the ability to handle complex joins, window functions, and analytical queries effortlessly.

Coding and Algorithms Unlike some DE roles that only require SQL, Meta expects proficiency in Python or Java. You will be tested on algorithmic problem-solving (similar to LeetCode Easy/Medium) but with a focus on data structures relevant to data processing, such as arrays, strings, and dictionaries.

System Design and Data Modeling You must show you can architect data systems from scratch. This involves designing schemas (dimensional modeling), choosing the right technologies (batch vs. streaming), and understanding the trade-offs in distributed systems (e.g., handling skew, partitioning, and shuffling).

Meta Culture and Behavioral Alignment Meta places heavy emphasis on their core values, particularly "Focus on Impact" and "Move Fast." You need to demonstrate how you handle ambiguity, manage stakeholders, and drive projects to completion in a fast-paced environment.

Interview Process Overview

The interview process for Data Engineers at Meta is rigorous, standardized, and designed to minimize bias while maximizing signal on your technical capabilities. Based on recent candidate experiences, the process is efficient but demands high endurance. You should expect a structured journey that typically moves from an initial screen to a comprehensive onsite loop.

Generally, the process begins with a recruiter screen to align on your background and the role's requirements. This is followed by a technical screen (video interview) focused heavily on SQL and possibly a short coding question. If you pass this stage, you will move to the "onsite" loop (usually virtual), which consists of 4–5 separate rounds covering Advanced SQL, Coding, System Design/Data Modeling, and a Behavioral interview (often called the "Jedi" round).

Candidates have reported that while the process is well-conducted, the difficulty can vary significantly depending on the specific team's focus (e.g., infrastructure vs. product analytics). Some candidates encounter deep-dive questions on distributed computing internals, while others face more standard pipeline design scenarios. It is crucial to maintain high energy throughout the loop, as decisions are made based on the consensus of all interviewers.

The timeline above illustrates the typical progression from your first contact to a final decision. Use this visual to plan your study schedule; you should aim to have your SQL and coding fundamentals solid before the Technical Screen, leaving the time between the screen and the onsite to practice complex System Design and Behavioral stories. Note that the "Onsite Loop" is the most draining day, often lasting 4–5 hours.

Deep Dive into Evaluation Areas

To succeed, you must go beyond surface-level knowledge. Based on recent interview data, Meta delves deep into the mechanics of how you process data.

SQL & Analytical Problem Solving

This is the highest-weight category. You will be given a business question and asked to write a query to answer it.

• Why it matters: It proves you can extract insights independently and accurately.
• Evaluation: Correct syntax, handling edge cases (NULLs, duplicates), and efficiency.
• Strong performance: Writing a query that works on the first try, using advanced features like RANK(), LEAD(), LAG(), and complex aggregations.

Be ready to go over:

• Complex Joins: Self-joins, cross joins, and joining multiple tables with different granularities.
• Window Functions: Calculating running totals, moving averages, and ranking within partitions.
• Data Cleaning: Handling string manipulation and timestamp conversions within SQL.
• Metric Calculation: Deriving retention rates, daily active users (DAU), and year-over-year growth.

Example questions or scenarios:

• "Calculate the daily retention rate of users based on their login history table."
• "Find the top 3 products per category by sales volume using a single query."
• "Identify users who have performed a specific sequence of actions within a 24-hour window."

Coding & Algorithms (Python/Java)

You will need to write functional code to solve data-centric algorithmic problems.

• Why it matters: Data Engineers build tools and custom transformations that SQL cannot handle.
• Evaluation: Logical correctness, code cleanliness, and time/space complexity (Big O).
• Strong performance: Solving the problem optimally and explaining your thought process clearly as you code.

Be ready to go over:

• Data Structures: Heavy focus on Dictionaries (Hash Maps), Arrays, and Sets.
• String Manipulation: Parsing logs, formatting data, or validating input strings.
• Logic Implementation: solving "easy" to "medium" complexity algorithmic challenges.

Example questions or scenarios:

• "Given a list of integers, move all non-zero elements to the left while maintaining order."
• "Write a function to parse a semi-structured log file and extract specific error codes."
• "Find the first non-repeating character in a stream of data."

Data Modeling & Distributed Systems

This area tests your architectural skills. You may face questions on schema design or deep technical questions on compute frameworks like Spark.

• Why it matters: You need to build pipelines that scale to petabytes without crashing.
• Evaluation: Ability to design star/snowflake schemas and understand distributed processing internals.
• Strong performance: Justifying your design choices (e.g., "I chose a wide transformation here because...") and explaining how data moves across a cluster.

Be ready to go over:

• Schema Design: Designing dimensional models (Facts and Dimensions) for a hypothetical app feature.
• ETL Architecture: Designing a pipeline from ingestion to reporting (Batch vs. Streaming).
• Spark Internals: Understanding the mechanics of distributed jobs.
• Advanced concepts: Narrow vs. Wide transformations, shuffles, partitioning strategies, and broadcast joins.

Example questions or scenarios:

• "Design the data model for a ride-sharing app's analytics system."
• "In Apache Spark, explain the critical performance difference between a narrow transformation like map and a wide transformation like groupBy. How does this impact data shuffling?"
• "How would you handle a dataset where one user has 100x more events than the average user (data skew)?"