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.

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)?"

Key Responsibilities

As a Data Engineer at Meta IT, your daily work revolves around enabling data-driven decisions through robust engineering. You will be responsible for the end-to-end lifecycle of data, from ingestion to consumption.

Your primary responsibility is building and maintaining scalable data pipelines. You will use internal tools (similar to Airflow) to orchestrate complex workflows that process massive datasets. This involves writing efficient ETL code, monitoring job performance, and troubleshooting failures in a distributed environment. You aren't just maintaining legacy systems; you are constantly refactoring and optimizing pipelines to improve latency and reliability.

Collaboration is a significant part of the role. You will work closely with Data Scientists to understand their analytical needs and ensure the data is structured correctly for their models. You will also partner with Software Engineers to define logging schemas and ensure high-quality data capture at the source. This cross-functional work requires you to translate vague business requirements into concrete technical specifications.

Additionally, you will drive data quality and governance initiatives. This means building automated checks to detect anomalies, ensuring compliance with privacy standards (a huge priority at Meta), and creating documentation that allows other teams to self-serve. You act as the custodian of data integrity for your domain.

Role Requirements & Qualifications

To be competitive for this role, you need a mix of strong technical fundamentals and practical experience with big data.

• Technical Skills:

  • SQL: Expert level. This is non-negotiable.
  • Programming: Proficiency in Python or Java is required. You should be comfortable writing production-level code, not just scripts.
  • Big Data Frameworks: Experience with Spark, Hadoop, or Hive is essential. Understanding how these systems work under the hood (memory management, shuffling) is critical for senior roles.
  • Workflow Orchestration: Experience with Airflow, Luigi, or similar scheduling tools.

• Experience Level:

  • Typically requires 3+ years of experience in Data Engineering, BI Engineering, or Backend Engineering with a data focus.
  • Experience working with cloud platforms (AWS, GCP, Azure) or large-scale on-premise clusters.

• Soft Skills:

  • Communication: Ability to explain complex technical concepts to non-technical stakeholders.
  • Autonomy: Proven ability to work independently in an ambiguous environment.
  • Problem Solving: A track record of identifying root causes of data issues and fixing them permanently.

• Nice-to-have Skills:

  • Experience with streaming data (Kafka, Flink).
  • Knowledge of data visualization tools (Tableau, Looker) to understand how end-users consume data.

Common Interview Questions

The following questions are representative of what you might face. While you won't get these exact questions, they illustrate the patterns and depth Meta expects. Use them to practice your problem-solving approach.

SQL & Data Analysis

• "Write a query to find the top 5 users by spend for each country in the last month."
• "Given a table of friendship requests (requester_id, accepter_id, status), calculate the acceptance rate for each day."
• "How would you identify duplicate records in a table that has no primary key?"
• "Calculate the month-over-month growth rate of active users."
• "Find all users who logged in on 3 consecutive days."

Coding (Python/Java)

• "Write a function to validate if a string is a valid IP address."
• "Given a dictionary of input data, transform it into a specific JSON output format."
• "Implement a function to merge two sorted lists into a single sorted list."
• "Find the most frequent element in an array."

System Design & Spark Internals

• "Design a data warehouse for Instagram Stories. How do you model the views and replies?"
• "We need to process 10TB of logs daily. How do you architect the pipeline to ensure data is available by 8 AM?"
• "Explain how a groupBy operation works in Spark. What happens during the shuffle phase?"
• "How do you handle late-arriving data in a streaming pipeline?"

Behavioral

• "Tell me about a time you identified a data quality issue that no one else noticed."
• "Describe a situation where you had a conflict with a Data Scientist regarding a data model. How did you resolve it?"
• "Give an example of a project where you had to learn a new technology quickly to deliver results."

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.

Frequently Asked Questions

Q: How difficult is the SQL round compared to other companies? The SQL round at Meta is widely considered one of the most difficult in the industry. It is not just about getting the right answer; it is about writing clean, optimized code quickly. You will likely use a plain text editor, so you cannot rely on autocomplete.

Q: Is the role remote or onsite? Meta has embraced a hybrid model, but expectations vary by team and location. Recent candidate experiences suggest that communication regarding location can sometimes be unclear. Ensure you clarify with your recruiter early in the process whether the role is "Remote," "Hybrid," or strictly "Onsite," especially if you are applying for a specific office location.

Q: Do I need to know specific tools like Dataswarm or Presto? No. While Meta uses internal tools (Dataswarm, Presto, etc.), they interview for general concepts. If you know Airflow, you can learn Dataswarm. If you know standard SQL/Hive, you can pick up Presto. Focus on the underlying concepts of ETL and distributed SQL.

Q: How long does the process take? The process can be relatively fast compared to other tech giants, often wrapping up within 3–5 weeks. However, scheduling the onsite loop can sometimes introduce delays.

Other General Tips

Think in Sets, Not Loops When writing SQL or designing pipelines, always demonstrate a "set-based" mindset. Avoid cursor logic or row-by-row processing. Interviewers look for candidates who understand how to manipulate data in bulk for efficiency.

Clarify Constraints Immediately In coding and system design rounds, never jump straight to the solution. Ask questions like: "What is the volume of data?", "Is real-time processing required?", or "How much data skew can we expect?". This shows you understand the engineering trade-offs.

Master the "Join" Mechanics For the hard technical questions (like the Spark shuffle question), understanding how a join happens physically on a cluster is key. Be prepared to explain broadcast joins vs. shuffle joins and when to use each.

Prepare for the "Jedi" Round Meta's behavioral interview is serious. Have prepared stories using the STAR method (Situation, Task, Action, Result) that specifically highlight your impact. Don't just say you "participated" in a project; explain what you drove and the quantitative result of your work.

Summary & Next Steps

Becoming a Data Engineer at Meta IT is a challenging but career-defining achievement. You will join a team that operates at the cutting edge of data infrastructure, solving problems that few other companies face. The role offers immense opportunity for impact, allowing you to build systems that shape the digital experience for billions of people.

To succeed, focus your preparation on advanced SQL fluency, algorithmic coding, and a deep understanding of distributed data systems. Do not underestimate the technical depth required; review the internals of tools like Spark and practice designing schemas from scratch. Approach the process with confidence, clear communication, and a focus on how your engineering skills drive business value.

The compensation data above reflects the competitive nature of this role. Note that Meta's packages are often heavy on RSU (Restricted Stock Unit) grants, which can significantly increase total compensation based on company performance. Ensure you discuss the full structure of the offer—Base, Bonus, and Equity—to understand the long-term value.

You have the roadmap. Now, it's time to execute. Good luck!