How to Prepare for Meta’s Data Engineering Interview
Breaking into Meta as a Data Engineer isn’t just about knowing SQL and Python—it’s about thinking like a Meta engineer from day one.
The interview process is designed to test not just your coding ability, but also your problem-solving approach, efficiency, and strategic thinking.
I recently went through the entire Meta Data Engineering interview process, from the recruiter call to the technical round and onsite interviews. If you're preparing for this journey, here’s what you need to know.
This article covers the general structure of the Meta DE interview and key preparation tips.
If you want the exact breakdown of how many questions to expect, how to tackle each one, and insider insights on my experience — subscribe to the paid series.
📌 The Meta Data Engineering Interview Process
1️⃣ Recruiter Call (Screening Interview)
Duration: ~30 minutes
Goal: Verify basic qualifications, assess your approach to problem-solving.
You'll be asked high-level SQL & Python theory questions, such as:
What’s the difference between
UNIONandUNION ALL?How does a
LEFT JOINdiffer from aRIGHT JOIN?When would you use
WHEREvsHAVINGin SQL?Python basics: Data structures, list comprehensions, and simple algorithms.
🚀 Tip: Keep your answers concise and structured. The recruiter is checking for clarity, not depth.
2️⃣ Technical Interview (SQL + Python Coding Round)
Duration: ~60 minutes
Goal: Test your coding ability and efficiency in SQL & Python.
This round consists of 4 SQL questions + 4 Python questions—but speed is key. You'll be expected to complete all questions within the time limit.
Topics covered:
🔹 Writing complex SQL queries (JOINs, aggregations, filtering, ranking).
🔹 Optimizing query performance.
🔹 Writing Python scripts to manipulate data.
🔹 Solving algorithmic problems efficiently.
🚀 Tip: Practice coding under time constraints—Meta values speed + accuracy over perfection.
3️⃣ Onsite Interview (4 Rounds of Deep-Dive Questions)
Duration: ~4 hours
Goal: Assess your real-world data engineering capabilities.
The onsite loop consists of four rounds:
Data Modeling Interview – Designing efficient, scalable database structures.
ETL Round 1 – Writing SQL/Python to transform and process large datasets.
ETL Round 2 – Optimizing ETL workflows for performance.
Ownership & Problem-Solving – Evaluating how you handle ambiguous data challenges.
🚀 Tip: Think out loud! Meta interviewers want to understand your approach—not just see the final answer.
🌟 How to Prepare for Meta’s Data Engineering Interview
✅ Step 1: Master SQL & Python Fundamentals
Meta uses PostgreSQL, but standard ANSI SQL knowledge is enough. You should be comfortable with:
Writing efficient SQL queries.
Handling NULL values, filters, aggregations, and window functions.
Writing Python scripts for data manipulation.
📀 Resources: LeetCode, HackerRank, and Meta’s official practice guides.
✅ Step 2: Solve Problems Under Time Pressure
You’ll need to code fast and accurately. Use a stopwatch to time yourself while solving SQL & Python challenges.
Practice challenges:
🔹 Write a query to rank users by activity in a database.
🔹 Optimize an inefficient Python function that processes millions of rows.
🔹 Implement a data pipeline transformation using SQL & Python.
✅ Step 3: Understand Meta’s Data Culture
Meta is data-driven, and engineers are expected to think strategically. Familiarize yourself with:
Meta’s product decisions based on data.
How A/B testing and data experimentation work at scale.
Best practices for building scalable data pipelines.
📀 Tip: Check out Meta’s blog posts & engineering case studies!
🔒 Want the Exact Questions & Insider Breakdown?
This article gives a high-level prep guide, but if you’re serious about cracking Meta, here’s what’s inside the paid series:
👉 Paid Subscription:
🔹 Exact number of questions for each round.
🔹 How to answer every question step-by-step.
🔹 Detailed breakdown of the four onsite rounds (Data Modeling, ETL, Ownership).
👉 Elite Career Accelerator:
🔹 Real Meta interview questions I faced.
🔹 My solutions & mistakes (what worked, what didn’t).
🔹 Exclusive Q&A for members.
🚀 Ready to crack Meta? Subscribe for insider access.