Meta Data Engineer Interview Questions and Preparation Guide

Landing a Meta data engineer interview is exciting but also intimidating. Meta’s scale means the interview process is about proving you can handle messy, real-world data problems under time pressure.

Unlike many other tech companies, Meta’s data engineer interviews focus less on obscure algorithms and more on fundamentals: writing clean SQL for product analytics, manipulating Python data structures, designing fact/dimension schemas, and showing strong product sense. Candidates consistently say the hardest part isn’t the difficulty of the questions, but the clock ticking down while parsing tricky business scenarios.

In this guide, we’ll walk through the Meta data engineer interview process, the types of questions you can expect, and how to prepare effectively. By the end, you’ll know not only what questions may come up, but also how Meta evaluates your thinking and how to showcase the skills that get you hired.

Meta Data Engineer Interview Process Overview

Before going into the Meta data engineer interview questions, let’s understand the overall process of the interview. The interview typically moves through three stages:

1. Recruiter Screen

• A 20–30 minute chat where the recruiter confirms your background, skills, and motivation.
• You’ll hear how many rounds to expect and what topics will be covered.

2. Technical Phone Screen

• Usually 1 hour; split into two parts
  • ~ 25-30 minutes of SQL
  • ~ 25-30 minutes of Python
• Expect 3-5 SQL questions and 3-5 Python questions, each tied to business-style scenarios. The schema is provided, and the code doesn’t need to be perfectly runnable — what matters is logical correctness and clarity of approach.

3. Onsite (Full Loop)

• 4-5 interviews across one day, covering:
  • SQL & Python (similar in style to the phone screen but slightly more complex).
  • Data Modeling (designing fact/dimension schemas, reasoning about grain, keys, and trade-offs).
  • Product Sense & Analytics (choosing metrics, designing dashboards, explaining why certain KPIs matter).
  • Behavioral / Ownership (stories showing leadership, collaboration, and impact).
• Some loops include a lunch/chat with a potential teammate, which is informal but still part of your evaluation.

What Meta evaluates

• Fundamentals over tricks: clean joins, CTEs, group-bys, Python dictionaries and lists, not niche functions or advanced algorithms.
• Communication: think out loud, ask clarifying questions, and show structured reasoning.
• Handling pressure: candidates repeatedly mention that the real challenge is answering clearly and quickly in a time-boxed setting.
• Coachability: Interviewers often drop hints; catching and applying them is seen as a strength, not a weakness.

Screening Round: SQL & Python

The screening is often the most stressful part of the Meta data engineer interview because it sets the tone for the rest of the process. You’ll typically face 5 SQL questions and 5 Python questions, each framed as a practical business problem.

The questions themselves aren’t extremely complex, but the real challenge is solving them quickly, under pressure, while explaining your reasoning clearly.

SQL in the Screening Round

Meta’s SQL questions are built around fact and dimension tables (for example, sales transactions with author, customer, and payment dimensions).

Expect to use joins, aggregations, window functions, CASE statements, self-joins, and CTEs. The schema is provided, but the questions are deliberately worded in ways that require careful interpretation.

Common Meta data engineer interview question examples:

• Identify authors who have published at least 5 books.
• Calculate the percentage of sales completed on the same day a customer registered.
• Find the IDs of the top 5 customers, ordered by average payment per book made by people they invited.

Example solution

Find authors who have published at least 5 books.

sql

WITH author_counts AS (

    SELECT
    a.author_id,
    COUNT(b.book_id) AS book_count
    FROM authors a
    JOIN books b
    ON a.author_id = b.author_id
    GROUP BY a.author_id
)

SELECT author_id
FROM author_counts
WHERE book_count >= 5;

Here, the CTE makes the logic readable. The key is grouping by author, counting, and filtering with HAVING or a subsequent filter.

How to approach such questions:

• Clarify the schema first. Even if you think you understand, asking questions shows you’re thoughtful and prevents missteps.
• Use CTEs to break problems down. Meta values readability over squeezing everything into a single query.
• Think in terms of business meaning. For instance, when asked about “same-day sales,” consider what “same day” means in relation to the customer registration timestamp.
• Communicate throughout. Talk through the join keys, the filtering logic, and why you’re grouping on certain columns.

Python in the Screening Round

Python questions test your ability to manipulate collections, and not your ability to implement advanced algorithms. Most focus on lists, strings, dictionaries, sets, and simple iteration logic.

Typical examples include:

• Calculate the average book price from a list of prices.
• Given customer comments from multiple locations, find the most common comment across all locations (ignoring duplicates from the same location).
• Determine how many meetings can overlap at the same time given start and end times.
• Construct the largest number possible from the digits of a given integer.

Example solution:

Given customer comments from multiple locations, find the most common comment across all locations (ignoring duplicates from the same location).

python

from collections import Counter

def most_common_comment(comments_by_location):

    seen = set()

    flat_comments = []

    for location, comments in comments_by_location.items():

        for c in set(comments): # remove duplicates within the same location
            if (location, c) not in seen:
                flat_comments.append(c)
                seen.add((location, c))

    counts = Counter(flat_comments)

    return counts.most_common(1)[0][0]

The solution shows practical skills: deduplicating per location, then counting across all. The interviewer cares more about process and clarity than whether you handle every edge case perfectly.

How to approach:

• Define the right data structure upfront. For frequency counts, use a dictionary; for uniqueness, lean on sets.
• Don’t over-optimize. Built-in functions like sort, max, or Counter are perfectly acceptable.
• Explain trade-offs. For example, when finding overlapping meetings, clarify why you’d use sorting or a min-heap depending on scale.
• Ask clarifying questions. The wording can be intentionally ambiguous, so confirming details saves time.

Full-Loop Onsite: Deep Dives

If you clear the screening, you’ll be invited to the full onsite loop. This stage has 3–4 technical interviews plus 1 behavioral/ownership round. While the style is consistent with the screening, the problems go deeper into real-world business reasoning.

Product Sense & Analytics

One of the most distinctive parts of the Meta data engineer interview is the product sense round. You’ll be asked to think like a data partner for a product team. For example, helping Instagram measure engagement or evaluating retention in Facebook Groups.

What to expect:

• Brainstorming 4–6 metrics that matter (e.g., DAU/MAU, retention, conversion, revenue per user).
• Defining both numerator and denominator, and justifying why you chose them.
• Follow-up: The interviewer will push if a key metric is missing.
• Dashboard design: when to use a line chart vs. a bar chart, what breakdowns matter.

How to prepare:

• Practice breaking down a product into user actions, funnel steps, and value drivers.
• Be explicit about assumptions (e.g., how you define “active” or “retained”).
• Don’t just list metrics. Tie them back to product goals.

Data Modeling

Data modeling is another common onsite module. You’ll be asked to design a schema for a product scenario such as a ride-sharing app or bookstore system.

Key areas interviewers look for:

• Facts and dimensions: state the grain of your fact table clearly.
• Primary keys and foreign keys: define relationships (1-to-M, M-to-M).
• Slowly Changing Dimensions (SCD2): show you know how to preserve history.
• Bridge tables and role-playing dimensions: explain trade-offs.

Tips:

• Start by clarifying the grain (e.g., “a row per ride completed”).
• Offer more than one design if appropriate, and compare pros/cons.
• Keep performance in mind. Sometimes denormalization makes sense at Meta’s scale.

SQL & Python (Onsite)

You’ll see SQL and Python questions again, but slightly more complex. The schema is provided, but the output is what matters. The queries don’t need to be executable.

SQL focus areas:

• Grouping and filtering with SUM(CASE…).
• Multiple joins, including LEFT JOIN and FULL OUTER JOIN.
• Window functions like LAG and LEAD.
• Use of COALESCE for missing values.

Python focus areas:

• Business-oriented data logic (e.g., “find top commenters on a restaurant review page”).
• Heavy use of dicts, lists, sets, tuples, and sorting.
• Familiarity with queue/stack structures for concurrency problems (meeting overlaps, maximum simultaneous users).

The biggest trap candidates fall into here is overthinking. Meta interviewers don’t want to see clever algorithm puzzles solved from scratch. They want clear, practical problem-solving using basic Python and SQL building blocks.

Behavioral / Ownership

The behavioral interview (sometimes called the “ownership” round) is critical. Meta assesses candidates on signals like taking initiative, driving impact, collaborating across teams, and learning quickly.

How to prepare:

• Have 1–2 strong stories for each of the five core signals Meta evaluates.
• Use the STAR format (Situation, Task, Action, Result) and include quantifiable metrics (e.g., “increased data pipeline efficiency by 40%”).
• Be ready to adapt your story. If the question doesn’t fit exactly, shape your example so it still highlights ownership or impact.

Meta’s behavioral interviewers will push with follow-ups if your first answer doesn’t land. They’re looking for depth, not surface-level anecdotes.

Culture & Lunch Chats

Some loops include a casual lunch or coffee with a teammate. While less formal, treat it as part of the interview. It’s a chance to show curiosity about the team’s challenges, tech stack, and culture.

Preparation Framework & Study Plan for Meta Data Engineer Interview

You don’t need to grind LeetCode to prepare for the Meta data engineer interview. You need to master fundamentals and learn to apply them under time pressure. A strong prep plan balances technical drills, product thinking, and behavioral storytelling.

Study Allocation

• SQL (30–40%): Practice business-style queries with joins, aggregations, window functions, and CTEs.
• Python (30–40%): Focus on list, dict, set operations, sorting, and simple iteration patterns.
• Data Modeling & Product Sense (20%): Review schema design principles and practice structuring metrics.
• Behavioral / Ownership (10%): Prepare leadership and impact stories with quantifiable results.

Practice Strategies

Simulate the clock. Practice solving random SQL/Python problems in 5–10 minutes. This builds the ability to parse tricky wording under stress.

Avoid memorization. Relying on repeated exposure to the same problems can backfire. Meta often rephrases or twists familiar setups. Instead, train adaptability by tackling unseen questions.

Think out loud. Verbalizing logic makes it easier for interviewers to guide you with hints and catching those hints is considered a positive signal.

Review schemas. Be ready to work with transaction facts and dimensional setups. Always clarify the grain before querying.

Prepare flexible behavioral stories. Meta interviewers may reframe questions; adapt your story on the fly while still highlighting ownership and measurable impact.

Weekly Prep Breakdown (4–6 weeks)

Conclusion

Cracking the Meta Data Engineer interview isn’t about memorizing a fixed set of SQL or Python problems. The real test is whether you can think clearly under pressure, communicate your reasoning, and connect data work to product impact.

The most successful candidates treat the interview less like an exam and more like a collaborative problem-solving session. Explain your thinking, ask clarifying questions, and don’t be afraid to take hints. Meta values learning agility as much as correctness.

With focused SQL and Python practice, a few strong product metrics frameworks, and well-prepared behavioral stories, you’ll be ready to walk into your Meta Data Engineer interview with confidence.

Ready to Take the Next Step?

If you’ve made it this far, you already know the Meta Data Engineer interview is about showing you can think like a data engineer. But preparation doesn’t end here. The best way to sharpen your skills is to practice on real interview problems, learn modern data engineering patterns, and see how GenAI is reshaping pipelines and systems in 2025.

That’s exactly what you’ll find in the FAANG Data Engineering Masterclass, a program built for engineers aiming at top roles in FAANG+ and beyond. From live problem-solving sessions to AI-powered data workflows, you’ll get frameworks and strategies you can use immediately in your prep and on the job.

Led by a FAANG data engineer, this masterclass blends technical depth with real-world career coaching. Whether you want to master advanced SQL design, understand how GenAI integrates with pipelines, or get insider strategies for interviews, this is the structured path to level up.

FAQs: Meta Data Engineer Interview

1. How hard is the Meta Data Engineer interview?

The interview is challenging mainly due to time pressure and ambiguous wording, not overly complex algorithms. Clear communication and structured problem-solving matter most.

2. What SQL topics should I focus on for the Meta Data Engineer interview?

Expect medium-level SQL covering joins, aggregations, CTEs, window functions, and case statements, all applied to real business scenarios.

3. Do I need to know advanced algorithms for the coding rounds?

No. The Python section emphasizes lists, dictionaries, sets, and problem-solving with business logic rather than complex data structures or algorithms.

4. How is the onsite round different from the screening?

On-site questions resemble the screening but add data modeling, product sense, and ownership interviews to evaluate system design, metrics, and collaboration skills.

5. How important are behavioral questions at Meta?

Very important. Meta values ownership and impact stories. Be ready with concrete examples showing leadership, problem ownership, and measurable results.