Meta Data Engineering Manager Initial Technical Screen Interview Guide

Preparing for this round that focussed wholly on whether you can still write SQL is the right instinct applied to the wrong question.

The Meta Data Engineering Manager initial technical screen is not verifying whether you can perform at IC level.

It is verifying whether you have stayed close enough to the work to lead it with technical authority. A manager who cannot write a correlated subquery, model a dimensional schema against a defined set of business questions, or structure a Python function to process a dataset cannot credibly review their team's output, identify a flawed data model before it ships, or make the instrumentation decisions that determine what product analytics are even possible downstream.

That is the specific capability this 45-minute session is designed to confirm, and it is a meaningfully different frame than treating it as a syntax recall exercise.

The second thing worth understanding before the round begins is that the three skill areas, SQL, Python, and data modeling, are not arbitrary. They mirror the day-to-day technical work of a data engineer at Meta precisely. The screen is a compressed simulation of real data engineering work, not an abstract technical assessment.

This guide is built on Meta's own internal preparation materials for this round and informed by Prepfully coaches who are current Data Engineering leaders at Meta, including a Director of Data Engineering.

For context on the full interview process, see the Meta Data Engineering Manager Interview Guide.

The initial technical screen is a 45-minute session conducted via CoderPad with a Data Engineering Manager or senior individual contributor at Meta. The session covers three skill areas in sequence: SQL, Basic Python Coding, and Data Modeling. Your coding language for the Python portion is your choice of Java, Python, or C. Confirm your preferred language with your recruiter before the session so they can inform your interviewer.

CoderPad has no syntax highlighting, no autocomplete, and no error messages. Code execution is disabled, so you cannot run your code to verify it. What that means in practice is that correctness has to come from your reasoning, which is exactly the point. Minor mistakes are not held against you, because interviewers are looking for signal on judgment (and because nobody gets every semicolon right when someone is watching).

Prepfully's Meta DEM coaches recommend that tell your interviewer upfront how comfortable you are in each of the three skill areas. At this level, that clarity is not vulnerability. It sets expectations, helps the interviewer calibrate, and gives you room to show how you think. If you forget a syntax detail mid-session, just ask. Interviewers expect it and it does not affect your score.

Meta's evaluation for this round names three focus areas: SQL, Basic Python Coding, and Data Modeling.

If your performance sits close to the cutoff in this round, Meta may schedule an additional technical interview before making a decision. Your recruiter will communicate this if it applies.

SQL: Set-Based Thinking and PostgreSQL Queries

Meta is assessing your ability to approach data problems using a set-based rather than procedural mindset, using a PostgreSQL database.

Your interviewer presents a series of business questions against a provided data model and you write the SQL to answer them. The concepts covered include all join types, inner, left and right outer, full outer, and cross, correlated subqueries, aggregations, WHERE versus HAVING, NULL value handling, and case statements.

The set-based framing is worth understanding before you walk in. A procedural approach thinks row by row, iterating through data to arrive at an answer. A set-based approach thinks in terms of groups, filters, and relationships between collections of data, which is how SQL was designed to be written and how Meta's data engineers actually work.

If you default to procedural thinking under pressure, your queries will be slower and more complex than they need to be, and that gap is visible to an interviewer who works with data at Meta's scale every day.

Reported candidate experiences from this round specifically name window functions, self-joins, full outer joins, CTEs, sum(case) patterns, coalesce for NULL handling, and lag as constructs that came up alongside standard join and aggregation questions.

Worth knowing: Meta uses Presto, now known as Trino, as its primary internal query engine. The interview uses standard SQL syntax rather than anything Presto-specific, so your standard SQL preparation is exactly what you need and there is no additional dialect to worry about.

Basic Python Coding: Fundamentals Under Time Pressure

The Python bar in this round is not software engineering-level expertise, and Meta's own materials are clear about this. The goal is to demonstrate that you can code a loop that iterates and solves a problem, using control structures, variables, common data structures including arrays and lists, maps and dictionaries, and tuples, and functions. The code needs to run and pass the test cases.

Reported candidate experiences describe the Python portion as a speed test more than a complexity test. Dictionaries, lists, sets, tuples, and basic sorting patterns are the recurring data structures. Queue and stack structures have come up in concurrency problems involving maximum simultaneous users or meeting overlap scenarios, with LeetCode's Meeting Rooms problems cited specifically as useful preparation. The trap most people fall into is overthinking. These problems are designed to be solved with fundamentals.

Because execution is disabled, you can and should ask your interviewer if you are unsure about syntax. This is not a negative signal. It is what the round is designed to accommodate.

Data Modeling: Access Pattern Design

The data modeling exercise is the most differentiating part of the Meta data engineering manager technical screen and consistently the least prepared for. You are given an unstructured dataset and five business questions upfront, and asked to design a data model that can efficiently answer all five. The session is a high-level design exercise, agnostic to specific tools or technologies.

The five-questions-first structure is intentional and reveals the underlying skill being tested: whether you think about access patterns before you design schemas. Most people prepare data modeling by drawing entity-relationship diagrams and then working out what questions the model can answer. The correct order of operations, and the one this exercise enforces, is the reverse. Understand what questions the data needs to answer, then design the model to support those access patterns efficiently.

Reported candidate experiences specifically cite Kimball fundamentals, slowly changing dimension types, bridge tables, fact grain decisions, role-playing dimensions, and the trade-offs between different fact table designs as concepts that came up. The ability to explain why you are making a modeling choice is scored alongside the choice itself.

Practice by taking a product you know well, News Feed, a marketplace, a streaming platform, and working through what entities matter, what attributes each needs, and what the dimensional model should look like to support a defined set of business questions. The Kimball Group's Dimensional Data Modeling techniques, specifically chapters 2 and 3 of The Data Warehouse Toolkit, are the most targeted preparation for the modeling concepts that surface in this round.

Understanding how Meta's individual contributors are expected to approach these same three skills gives useful context for the technical bar you are leading to. The Meta Data Engineer Interview Guide covers the IC version of this process in detail.

Tell your interviewer where you stand in each skill area before the first question. Meta's own materials recommend this explicitly. For a manager, this is one of the clearest signals of good judgment you can send in the first two minutes. It calibrates the conversation, removes the adversarial dynamic, and gives you space to demonstrate how you approach a problem rather than whether you can recall syntax under pressure.

The data modeling exercise is where most managers leave the most points on the table. SQL and Python tend to stay reasonably sharp because managers encounter them in review and discussion regularly. The data modeling exercise requires the specific kind of from-scratch thinking that gets rusty when you have been directing modeling decisions rather than making them yourself. The access-pattern-first mindset, working through all five business questions before touching the schema design, is the distinction between demonstrating data engineering judgment and demonstrating schema-drawing ability.

Think out loud throughout the session. Interviewers in the Meta data engineering manager technical screen are not evaluating only whether your output is correct. They are evaluating how you approach the problem, where you ask clarifying questions, and how you reason through trade-offs. Interviewers are specifically trained to offer hints when you head in a suboptimal direction, and responding to those hints is scored as a positive signal. Candidates who go silent and produce a finished block of code give the interviewer almost nothing beyond the correctness of the output.

Clarify before you write. Whether it is a SQL question, a Python problem, or the data modeling exercise, making sure you understand what is actually being asked before you start writing is both correct technical practice and a direct signal to the interviewer that you work the way Meta's data engineers are expected to work. The parallel to real practice is direct: you would not build a pipeline or design a data model without first understanding the business question it is meant to answer.

The three skill areas in this round mirror precisely the work your team does every day. How you perform in it tells an interviewer something real about how close you have stayed to that work as you have moved into leadership.

The following questions are drawn from reported candidate experiences specific to this round.

SQL

• Given a table of employees with department, salary, and hire date, write a query to rank employees by salary within each department, handling ties correctly.
• For a given sales table, compare the percentage of total sales completed on the first and last working day of the month.
• Write a query to identify users who made transactions in the last 30 days but not in the 30 days before that, using appropriate join and date logic.
• Given a self-referential employee and manager table, return each employee alongside their manager's name, handling cases where a manager record may not exist.
• Write a query to find the top five products by total sales, using a CTE and handling NULL values in the aggregation correctly.

Basic Python Coding

• Given a list of integers, write a function to determine whether the list is monotonically increasing or decreasing.
• Given a list containing NULL values, write a function to replace each NULL with the previous non-NULL value in the list.
• Find the maximum number of simultaneous users active at any point, given a list of session start and end times.
• Given streaming and batch data inputs, write a function to process each and return a unified output in a consistent format.
• Write a function that takes a string and returns the count of each unique character, using a dictionary.

Data Modeling

• You are given an unstructured dataset and five business questions upfront. Design a data model that can efficiently answer all five, identifying the appropriate fact and dimension tables, their grain, primary and foreign keys, and the relationships between them.
• How would you model a social platform where users post content, leave comments, and react to posts? What fact and dimension tables would you create, and what trade-offs did you consider in your grain decisions?
• Design a data model for a ride-sharing product. What entities matter, what attributes belong in each dimension, and how would you handle slowly changing dimensions for driver and rider attributes?
• Given a bookstore scenario with authors, books, customers, and sales, design a dimensional model to support a defined set of business analytics questions, and explain why you chose the fact grain you did.

Establish your actual baseline across all three skill areas

Before deciding where to spend your preparation time, assess where you are in each area right now under the conditions that matter. SQL you write daily will feel different from SQL you reviewed in someone else's work six months ago. Python fundamentals that have not been used hands-on in a year will feel different under a time constraint with no IDE. The data modeling exercise will feel different if your recent experience has been directing modeling decisions rather than making them from scratch. The specific gap to identify is between knowing something and executing it quickly in a plain text editor without autocomplete, because that gap is where most managers discover where they need work.

Prepare SQL for set-based thinking, not syntax recall

Practice writing queries against real business scenarios, not abstract SQL exercises, and practice doing it without running the queries to check them. Window functions, correlated subqueries, CTEs, full outer joins, sum(case) patterns, coalesce for NULL handling, and lag are the constructs that appear most consistently in reported candidate experiences from this round. StrataScratch has a Meta filter that surfaces questions specifically calibrated to the style of SQL Meta uses in its interviews, which is more targeted preparation than general LeetCode SQL.

Practice Python on fundamentals under a clock

Dictionaries, lists, sets, tuples, and simple iteration patterns are the foundation of the Python questions in this round. Practice writing Python functions from scratch without an IDE, under a time constraint, focused on making the code correct and the logic clear. For concurrency-type problems involving time ranges and overlap, LeetCode's Meeting Rooms problems are specifically cited by candidates who have cleared this round as useful preparation.

Approach data modeling as an access pattern problem, not a schema exercise

Read all five business questions before touching the schema design. Work through what the key entities are, what attributes each needs, what the appropriate grain is for each fact table, and what the dimensional structure should be to support those specific questions efficiently. Practice explaining your choices as you go, including the trade-offs you considered and rejected. Kimball's Dimensional Data Modeling fundamentals, specifically chapters 2 and 3 of The Data Warehouse Toolkit, are the most targeted preparation for the specific modeling concepts that come up in this round.

Practice the full round format before the real session

The specific combination of three skill areas in 45 minutes in a plain text environment without execution feels different in practice than it reads on paper. Prepfully's mock interviews for this role pair you with a Meta Data Engineering Manager for a live, scored simulation of this round. You get detailed feedback on every skill area and a hiring decision at the end of the session based on your current performance. Schedule a mock interview.

Interview prep:

• Meta Data Engineering Manager Interview Guide
• Meta Data Engineering Manager Interview Question Bank
• Meta Data Engineering Manager Mock Interview Coaches

SQL and coding resources:

• StrataScratch — Meta SQL questions with Meta filter
• HackerRank SQL
• LeetCode
• CoderPad practice environment

Data modeling resources:

• The Data Warehouse Toolkit, Kimball Group — Chapters 2 and 3
• Engineering at Meta

Role-specific prep:

• Meta Engineering Manager Interview Guide
• Meta Data Engineer Interview Guide
• Culture at Meta