Meta Data Engineer Onsite Interview: The Ultimate Guide
The Ultimate Playbook for Cracking Meta’s Data Engineering Onsite
Hey Data Engineers! 👋
Welcome to the Elite Career Accelerator Plan—where we break down high-impact Data Engineering interview strategies that set you apart in top tech interviews.
Over the past few weeks, we’ve been deep-diving into Meta’s Technical Interview Series—covering SQL, Python, and ETL deep dives.
🔥 Today, we’re unlocking the final piece— the Meta Onsite Interview Guide with a real-world breakdown of ETL, Ownership, and System Design Interviews.
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So, you made it to the onsite at Meta—congratulations! 🎉
By now, you’ve already crushed the technical screen, proving your SQL and Python fundamentals. But here’s the catch: this next stage isn’t about grinding Leetcode anymore.
The onsite is where Meta shifts gears—it’s no longer just about syntax and coding speed. Now, it’s about how you think as a Data Engineer.
💡 Can you design scalable solutions?
💡 Do you make the right trade-offs under constraints?
💡 Can you communicate your thought process clearly?
This round is where real engineering meets business impact.
Let’s break it all down—what to expect, how to approach it, and an in-depth walkthrough of an actual Meta interview.
How to Approach the ETL Interviews
Each Data Engineering onsite interview follows a structured full-stack approach:
✅ Product Sense – Understanding the problem and clarifying assumptions.
✅ Data Modeling – Designing an efficient schema.
✅ ETL & SQL – Writing a scalable, maintainable query.
✅ Python Coding – Implementing business logic & optimizations.
✅ Data Visualization & Trade-offs – Making engineering decisions.
Expect 3 interviews, each testing a different business scenario.
👉 This is not a theoretical test. You need to think like an engineer at Meta—balancing performance, data consistency, and real-world constraints.
Now, let’s dive into an interview-style example.
Cracking the ETL Interview: A Real-World Walkthrough
Product Sense: Understanding Daily Active Users Trend
Let’s assume you’re given a Daily Active Users (DAU) dashboard for Messenger, displaying trends over the past 60 days. You notice something unusual—a sudden spike at Day 30 followed by a significant drop at Day 50.
Interviewer: How would you investigate to explain this shift?
To properly investigate the DAU spike at Day 30 and the drop at Day 50, I would take a structured approach to analyze the data and identify potential causes. Here's how I would break it down:"
🔍 Step 1: Clarify the Scope
Before diving into analysis, I’d first confirm what exactly we mean by Daily Active User (DAU) in this scenario.
📌 What defines an "active" user?
Does it include only app opens, or do actions like sending messages, reading messages, or reacting to messages count?
Are we tracking all platforms (iOS, Android, Web)?
Are we distinguishing between new users vs. returning users?
📌 What is the time frame of the change?
We know this is a 60-day dataset, so I’d want to check if similar fluctuations have happened before.
Is this a seasonal pattern?
Could this be tied to predictable user behavior, like school starting, holidays, or cultural events?
Since you mentioned it’s a sudden increase and decrease, it’s likely not seasonal.
📊 Step 2: Identify Possible Causes
To systematically diagnose the shift, I’d structure my investigation around four key areas: