What is a Data Engineer at Meta?

At Meta, the Data Engineer role is pivotal to the company’s ability to serve billions of users across Facebook, Instagram, WhatsApp, and Reality Labs. You are not simply a mover of data; you are the architect of the information ecosystem that drives product decisions, machine learning models, and strategic business insights. This role sits at the intersection of software engineering and data science, requiring you to build scalable, reliable, and efficient data infrastructure.

Data Engineers at Meta are responsible for the "productionalization" of data. You will design logging frameworks, build massive ETL pipelines, and create dimensional data models that allow Product Managers and Data Scientists to query petabytes of data with low latency. Whether you are working on Product Analytics to optimize user engagement or Infrastructure to improve data center efficiency, your work directly impacts how the company understands its products and its users.

Getting Ready for Your Interviews

Preparation for Meta is distinct because the company values speed and execution as much as theoretical knowledge. You should approach this process not just by reviewing concepts, but by drilling your ability to solve problems under strict time constraints.

Your performance will be evaluated against these core criteria:

Technical Execution & Speed Meta places a premium on coding fluency. You are expected to write syntactically correct SQL and Python code rapidly. Interviewers look for candidates who can translate logic into code without constantly looking up syntax or struggling with basic libraries.

Data Modeling & Architecture You must demonstrate the ability to design data schemas that are scalable and efficient. Evaluators check if you can take an ambiguous product requirement (e.g., "build a table for video views") and design a normalized or denormalized schema that answers key business questions while handling massive scale.

Product Sense & Metric Definition Unlike pure backend engineering roles, a Data Engineer at Meta must understand the business. You will be evaluated on your ability to define meaningful metrics (e.g., "How do we measure success for Instagram Stories?") and debug data anomalies when metrics shift unexpectedly.

Ownership & Collaboration Meta’s culture relies heavily on "Ownership." You will be assessed on your ability to drive projects independently, navigate ambiguity, and collaborate with cross-functional partners like Data Scientists and Product Managers.

Interview Process Overview

The interview process for a Data Engineer at Meta is highly standardized and rigorous. It typically begins with a recruiter screening to assess your background and interest. If you pass this check, you will move to the Technical Screen. This round is famous for its format: it almost universally consists of 5 SQL questions and 5 Python questions to be completed within a single hour. This "5+5" format is a test of speed and accuracy; many candidates find the questions themselves to be of medium difficulty, but the time pressure makes them challenging.

If you succeed in the screen, you will proceed to the Onsite Loop (currently virtual). This usually consists of 4 to 5 rounds covering different competencies: Advanced SQL/Coding, Data Modeling, Product Sense, and a Behavioral/Ownership round. Throughout these rounds, the emphasis remains on practical application. You will rarely be asked abstract brain teasers; instead, you will face scenarios that mirror the actual work of a Data Engineer at Meta.

The timeline above illustrates the typical flow from application to offer. Note that the Technical Screen is the primary filter where most candidates are assessed on raw coding speed. The Onsite Loop shifts focus toward architectural thinking, product understanding, and cultural alignment.

Deep Dive into Evaluation Areas

Based on recent candidate experiences, the Meta Data Engineer interview is structured around specific technical pillars. Understanding these areas is essential for passing the loop.

SQL Proficiency (The "5 Questions" Standard)

This is the bread and butter of the interview. In both the screen and onsite rounds, you will be tested on your ability to write complex queries from scratch. The questions often start easy and ramp up in difficulty.

Be ready to go over:

• Joins and Filtering – Proficient use of INNER, LEFT, and FULL OUTER joins, often across 3+ tables.
• Aggregations – Calculating counts, sums, and averages, often grouped by multiple dimensions.
• Window Functions – This is critical. Expect to use RANK, DENSE_RANK, ROW_NUMBER, LAG, and LEAD to solve problems involving running totals or top-N records.
• Handling NULLs and Data Quality – Writing queries that robustly handle missing or messy data (e.g., using COALESCE).

Example questions or scenarios:

• "Calculate the percentage of users who clicked on an ad vs. those who viewed it."
• "Find the top 3 selling books per category for the last month."
• "Identify users who visited the site on three consecutive days."

Python & Scripting (The Other "5 Questions")

The Python portion of the interview tests your ability to manipulate data structures rather than your knowledge of complex graph algorithms. The focus is on "Data Structure & Algorithms" (DSA) applied to data tasks.

Be ready to go over:

• Dictionaries (Hashmaps) – You must be extremely comfortable using dictionaries for counting, grouping, and lookups.
• List & String Manipulation – Parsing strings, splitting data, and iterating through lists efficiently.
• Sets and Tuples – Knowing when to use a set for O(1) lookups or deduplication.
• Basic Algorithms – Binary search or two-pointer techniques may appear, but the focus is usually on logic and data transformation.

Example questions or scenarios:

• "Given a list of strings, return a dictionary counting the frequency of each word."
• "Find the common elements between two lists without using built-in intersection functions."
• "Parse a log file string to extract specific user IDs and timestamps."

Data Modeling

This round assesses your ability to design the foundation for analytics. You will be given a product scenario and asked to design the database tables.

Be ready to go over:

• Schema Design – Star schema vs. Snowflake schema, and when to use each.
• Dimensional Modeling – Defining Fact tables (events) and Dimension tables (attributes).
• Handling History – Slowly Changing Dimensions (SCD Type 1 vs. Type 2) to track how data changes over time.
• Scale Considerations – Partitioning strategies and handling high-cardinality data.

Example questions or scenarios:

• "Design a database schema for a library system tracking books, authors, and borrowers."
• "Create a data model for a ride-sharing app to track trips, drivers, and payments."
• "How would you model data for Google Classroom to track student assignments and grades?"

Product Sense & Metrics

Meta expects Data Engineers to understand why they are building pipelines. This round tests your business intuition.

Be ready to go over:

• Metric Definition – Defining success metrics (e.g., Daily Active Users, Retention Rate, Time Spent).
• Investigating Trends – How to debug a sudden drop in a key metric.
• Trade-offs – Balancing data accuracy with latency and storage costs.

Example questions or scenarios:

• "If comments on Facebook posts drop by 10% overnight, how would you investigate?"
• "We are launching a new feature for Instagram Stories. What metrics would you track to measure success?"

Key Responsibilities

As a Data Engineer at Meta, your daily work involves end-to-end ownership of data. You are responsible for conceptualizing and owning the data architecture for large-scale projects. This includes evaluating design trade-offs and operational costs to ensure systems are both efficient and scalable.

You will build and maintain the pipelines that feed into Meta’s massive data warehouse. This involves solving challenging integration problems using optimal ETL (Extract, Transform, Load) patterns. You will work with structured and unstructured data sources, ensuring that data flows reliably from production services into analytical tables.

Collaboration is a massive part of the role. You will work closely with Product Managers to understand the features they are building and with Data Scientists to ensure they have the clean, structured data needed for analysis. You are also the guardian of data quality; you will define Service Level Agreements (SLAs), implement privacy safeguards, and proactively fix broken pipelines to ensure trust in the data.

Role Requirements & Qualifications

Meta hires for potential and core engineering skills. The following qualifications are typically required to be competitive.

Must-have skills

• Advanced SQL: You must be able to write complex SQL queries fluently. This is the primary language for data manipulation at Meta.
• Coding Proficiency (Python/Java): Python is the standard for scripting and ETL tasks. You need strong command of data structures (dictionaries, lists) and basic algorithms.
• Data Modeling: Experience designing schemas (Star/Snowflake) and understanding normalization/denormalization trade-offs.
• ETL/Pipeline Experience: A track record of building and maintaining data pipelines (e.g., using Airflow, Dataswarm, or similar tools).

Nice-to-have skills

• Big Data Technologies: Experience with Spark, Hadoop, or Presto/Trino is highly beneficial given Meta's scale.
• System Design: Understanding of distributed systems and how to architect data flow for massive datasets.
• Product Analytics: Prior experience working directly with product teams to define metrics and track user behavior.

Common Interview Questions

The following questions are representative of what you might face. They are drawn from recent candidate experiences and reflect the "5+5" format (5 SQL, 5 Python) often seen in the technical screen.

SQL & Data Manipulation

• "Given a table of transactions, find the top 3 users by spend for each month."
• "Calculate the day-over-day percentage growth in video views."
• "Write a query to find all users who have sent a message but never received one."
• "Given a table of friend_requests, calculate the acceptance rate for each day."
• "How would you identify and remove duplicate rows from a table without a unique ID?"

Python & Coding

• "Write a function to flatten a nested dictionary."
• "Given a list of integers, find the two numbers that sum up to a specific target."
• "Parse a string of URL parameters and return them as a dictionary."
• "Write a script to validate if a given string is a valid IP address."
• "Implement a function to find the missing number in a sequential list from 1 to N."

Data Modeling & Design

• "Design the data tables for a music streaming app like Spotify."
• "How would you model the data for an e-commerce checkout system?"
• "Design a schema to track employee hierarchy and department history."

Behavioral & Ownership

• "Tell me about a time you identified a data quality issue before anyone else. How did you fix it?"
• "Describe a conflict you had with a Product Manager regarding a data request. How did you resolve it?"
• "Tell me about a complex data pipeline you built. What were the bottlenecks?"

Frequently Asked Questions

Q: Is the "5 SQL and 5 Python" structure for the technical screen strict? Yes, this format is highly consistent across recent interviews. You typically have about 60 minutes (sometimes split into two 25-30 minute blocks). You are expected to solve as many as possible. A common passing bar is solving at least 3 correctly in each section, though aiming for 4 or 5 is safer.

Q: How difficult are the coding questions compared to LeetCode? The Python questions are generally LeetCode Easy to Medium. They rarely involve complex dynamic programming or graph traversal. Instead, they focus heavily on string manipulation, dictionaries, and list processing—tasks that a Data Engineer actually does. The difficulty lies in the speed required, not necessarily the algorithm complexity.

Q: Can I use a language other than Python? While Python is the preferred language for the coding section and widely used at Meta, you can usually choose Java or Scala if you are more proficient in them. However, Python is often recommended because its concise syntax allows you to write code faster, which is a huge advantage in the time-constrained "5+5" round.

Q: What platform is used for the coding interview? Meta typically uses CoderPad or a similar browser-based IDE. Note: You often will not have access to auto-complete or syntax highlighting. You should practice writing code in a plain text editor to get comfortable with relying on your memory for syntax.

Q: How much does "Product Sense" matter for a Data Engineer? It matters significantly. Meta distinguishes itself by expecting DEs to be product partners, not just ticket-takers. You will likely have a specific round dedicated to this, where you must demonstrate that you understand what to measure (e.g., engagement, retention, churn) and how those metrics relate to business goals.

Other General Tips

Train for Speed and Accuracy The single biggest hurdle for most candidates is the time limit. Practice solving SQL and Python problems with a timer running. Aim to finish "Medium" difficulty SQL queries in under 5 minutes and Python data manipulation tasks in under 5 minutes.

Master the Dictionary In the Python section, the dictionary (hashmap) is your best friend. A large percentage of Meta’s DE coding questions can be solved efficiently using dictionaries for counting or lookups. Know the syntax for .get(), .items(), and dictionary comprehensions inside and out.

Ask Clarifying Questions Quickly In the Data Modeling and Product Sense rounds, the prompts will be intentionally vague. Do not jump straight into a solution. Spend the first 2-3 minutes clarifying the scope: "Are we tracking historical changes?", "What is the scale of this app?", "Are we optimizing for read-heavy or write-heavy workloads?"

Showcase "Ownership" In behavioral answers, focus on "I" rather than "We." Meta looks for individuals who take initiative. Highlight stories where you spotted a broken process, a data gap, or an efficiency problem and took it upon yourself to fix it without being asked.

Summary & Next Steps

Becoming a Data Engineer at Meta is a significant career milestone that places you at the center of one of the world's most data-rich environments. The role offers high impact, competitive compensation, and the chance to work with cutting-edge infrastructure. However, the interview process is demanding, specifically designed to test your coding speed, architectural foresight, and product intuition.

To succeed, focus your preparation on velocity. Drill standard SQL patterns (especially joins and window functions) and Python data structures until they are second nature. Don't neglect the Data Modeling round; being able to design a clean, scalable schema on a whiteboard is just as important as writing code. Approach the interview with confidence, demonstrating not just technical skill, but the proactive "ownership" mindset that defines Meta's culture.

The salary data above provides a baseline for what you can expect. Meta is known for strong compensation packages that include significant equity (RSUs) and performance bonuses. The exact offer will depend on your level (e.g., IC4, IC5) and location, but the role is consistently among the highest-paying in the industry for Data Engineering.

For more practice questions and detailed community insights, explore the additional resources available on Dataford. Good luck with your preparation—you have the roadmap, now it’s time to execute.