Meta Data Engineering Manager Interview
Detailed, specific guidance on the Meta Data Engineering Manager interview process - with a breakdown of different stages and interview questions asked at each stage
The role of a Meta Data Engineering Manager
As a Meta Data Engineering Manager at a company like Meta, you're essentially at the heart of how data shapes product development. The role is all about working with cross-functional teams—Product Managers, Data Scientists, and Software Engineers—to build and optimise the data infrastructure that supports product launches and drives key insights. You’ll be responsible for building out the architecture that informs decision-making, and your work will directly impact the growth of the company and the experience of over a billion users.
You’ll be working with one of the richest datasets in the world and leveraging cutting-edge technology to turn raw data into actionable insights. The stakes are high, but it’s also incredibly rewarding to see how your contributions impact the product on a global scale.
Meta offers great compensation for the data engineering manager role:
- Average Total Compensation: $405,000
- Base Salary: $238,167
- Stock Grant (per year): $143,667
- Bonus: $23,167
Meta Data Engineering Manager Interview Guide
The Meta Data Engineering Manager Interview process typically kicks off with a brief recruiter chat following which there are two elimination rounds:
- Leadership Screening
- Technical Screening
Once you make it through the first two rounds, you'll be scheduled for 4 or 5 interviews across the onsite round, which include:
- Technical Data Exercise — I
- Technical Data Exercise — II
- System Design Interview
- People Leadership Interview
- Ownership Interview
Leadership Screening
Overview
After the quick recruiter chat, the first real interview is the leadership Screening with the hiring manager. This one’s a 30–45 minute conversation which is a unique type of screening specific to Meta Data EM roles—where they really dig into your leadership experience and management style.
Tips
Here are 2 tips for this round:
- You can expect both situational and behavioural questions here, and you’ll definitely get the classic “Tell me about yourself” and “Why Meta?” questions—so be sure to practise them well.
- You'll also face hypothetical "what if" and situational "tell me about a time" questions; They’re looking for clear, detailed stories that showcase how you handle challenges and lead/grow teams and projects. Make sure you have specific examples ready about times you handled conflict, delivered a big achievement, or managed appraisals effectively. Also, Meta encourages that you answer in the STAR format (Situation, Task, Action, Result)—make sure you practise answering that way. We'd recommend practising this interview with a Meta data EM—you can schedule a “leadership” style mock Interview with one of Meta's Data Engineering Managers on Prepfully.
Interview Questions
- Give me an example of the toughest decision you've had to make?
- Describe a time when a team member was struggling. How did you handle it?
- In your opinion, what are the key qualities of an effective data engineering team or platform?
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Overview
In the Meta Data Engineering Manager interview, the next knockout round is the Technical Screening. It’s a 45-minute coding session on Coderpad (execution disabled) with SQL and a language of your choice (Java, Python, or C) — led by another manager or a senior IC.
You’ll typically get five questions covering
- Data modelling
- SQL, and
- Basic algorithms
For the manager role, they’re more interested in how you approach and think through the problem. The problems are often complex, so be ready to tackle bigger-picture issues. You’ll be expected to provide solutions in pseudo-code rather than fully executing code as opposed to standard DE roles that are expected to write and execute actual code.
Tips
- Since they care more about your approach, clearly outline your thought process and structure each solution logically. Think about the “why” behind each step.
- To give further insight, it’s very much a timed test of your problem-solving approach. It might sound robotic, but having some pre-scripted explanations or comments for typical steps (like indexing, joins, etc.) can help you stay clear and efficient. We'd recommend checking out standard questions on sites like LeetCode; have some common data engineering patterns or solutions practised in advance where you explain each step.
Interview Questions
- Given a recursive function to calculate the nth Fibonacci number.
- Write pseudocode to find the longest substring without repeating characters in a given string.
- Design a solution to store a list of users and quickly check if a new user is already registered.
- Write pseudocode to sort an array of integers using quicksort.
- Given a table of employees with columns for department, salary, and hire date, write a query to rank employees by salary within each department.
- Write an SQL query to retrieve the top 3 most purchased products for each user based on their order history.
- Draw an ER diagram for an e-commerce platform with users, products, orders, and reviews.
- Design a schema for a messaging app that supports users, messages, and chat groups.
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Overview
Coming to the onsite round—it typically starts off with a technical round which is usually broken into two rounds: the Tech Data Exercise I and Tech Data Exercise II.
Technical Round
- Technical Data Exercise I: This is a 45–60 minute technical interview where, after the introductions, you’re given a problem or feature and asked to brainstorm solutions. It’s a highly interactive session where you discuss different options to implement the solution. An example might be something like a streaming platform that wants to measure user engagement with specific content types. You’ll explore ideas on how to measure engagement and identify the viewing patterns of the audience. It’s a fun discussion, but keep in mind that, by the end, they’ll ask you to code part of your solution. Tip: Don’t suggest anything too complex to implement unless you’re sure you can code it up quickly.
- Technical Data Exercise II: This is another 45–60 minute technical interview, but here, you’re given a hypothetical business or app scenario and asked to define metrics that would be important to collect. For instance, something like: a social media platform wants to monitor user growth and activity. What metrics would you collect, and how would you design the schema? Here, you’ll need to design a database schema, usually a dimensional model, with entities/dimensions and facts/measures to support analytics. Toward the end, you’ll need to write SQL queries to retrieve the metrics you proposed. Tip: Practise designing schemas for different use cases, and make sure your SQL skills are sharp, especially with complex joins.
System Design
During this segment, they'll dive into your past experiences with designing and building systems or products—the focus will always eventually be on large-scale systems.
You'll need to lead the discussion on what the system should achieve, and then delve into specific areas to outline constraints and major components. They'll expect you to have a solid understanding of modern distributed systems design, especially factors like memory, disk, and latency capabilities.
People Leadership
After the System Design round, you’ll move on to the People Leadership round. Since you’ve likely already spoken with the hiring manager, introductions are usually skipped, and they’ll dive straight into questions. Your prep for the Leadership Screening will definitely help here.
A huge chunk of this round will focus on people management and cross-functional collaboration. The interviewer will be keen on digging into work experiences that you found super interesting or challenging, especially if it tied into what you see as an opportunity at Meta. Or times you've worked cross-functionally to break down barriers for your team or perhaps dived deep into team dynamics to boost productivity. So, have a neat set of 6–8 stories to give examples from that showcase your people management, conflict resolution, appraisals, coaching/mentorship, team-building, and talent acquisition skills. This basically means instances where you resolved critical technical issues, guided teams through technology transitions, etc. And don't forget to include examples of both successes and failures, mistakes you made, lessons you learned – they want the real deal. Again, STAR is the answering format that is expected.
And heads up—they sometimes throw in a programming or SQL question here. It’s usually pretty basic, designed to check your knowledge on ETL concepts or data querying basics, so brushing up on those won’t hurt.
Ownership
The final round in the Meta Data Engineering Manager interview process is the Ownership round. It is a 30-minute chat with a business stakeholder, where they focus on how you work with external teams and prioritise tasks when there are competing demands.
Since this round is non-technical and fairly short, it’s usually the least stressful of the interviews. The interviewer will want to know how you handle ownership of projects, how you collaborate with different stakeholders, and how you make decisions when priorities overlap.
After all the onsite rounds are done, the recruiter will get back to you with the panel’s decision once they’ve gathered and discussed everyone’s feedback.
Tips
Tips for System Design Round
- First -> First off, really get a handle on the problem. We can't stress this enough – tons of candidates dive straight into solution mode, but it's a huge mistake. Take a beat to clarify things, define your scope, lay out your goals, and only then explain your action plan. This will let the interviewer guide you where they want.
- Next – listen up for feedback. Interviewers know the scope is basically infinite. They've got a list of themes to hit during the interview, so pay attention. If they drop hints like "let's assume xyz isn't a constraint," that's your cue to shift gears. Or if they throw a curveball like "how about if this gets accessed worldwide daily," take it as your chance to dive into scaling strategies and maybe drop some knowledge on CDNs and caching.
- Third -> you're gonna be coming up with a bunch of ideas, and that's cool—encouraged, in fact. Just make sure you mention them, explain the tradeoffs, but then commit to a decision.
- Fourth → if you have specialized knowledge, like in kernels, file systems, networking systems, or JavaScript, be ready for questions rooted in those areas. The question might involve designing something you've never built before, and it's intentionally broad in scope, ensuring you won't cover every detail perfectly. So, having a strong foundation in system design principles and the ability to think critically about constraints and components is key.
- Finally -> there'll be moments when you hit a wall or aren't sure what the interviewer's fishing for. No big deal – it happens to everyone. The right move? Own up if you don't know. Ask if they want you to explore through some educated guesswork, but don't try to fake your way through.
Interview Questions
- Design a system to track engagement with articles on a news website. How would you measure engagement and identify popular content?
- If Meta wanted to track how users interact with stories on their platform, what metrics would you collect, and how would you implement them?
- Imagine an online marketplace wants to track abandoned carts and their causes. How would you set up a system to track this, and what insights would you look for?
- Imagine a gaming app wants to understand player engagement levels and session lengths. What metrics would you track, and how would you structure the data?
- For a food delivery app, what key metrics would you track to understand customer satisfaction? Design a schema for these metrics.
- A social media platform wants to monitor user growth and activity. What metrics would you collect, and how would you design the schema?
Leadership
- How do you deal with pressure?
- How do you handle differences of opinion?
- How do you motivate average team members?
- What do you think it takes to build a great Data Engineering team/platform?
- What is the most difficult thing you've had to do?
- Tell me about a time when you had to resolve a conflict within your team.
- Describe your management style.
- Can you share an example of a time you successfully mentored someone?
- How do you approach performance appraisals?
- Describe a time when you had to make a tough decision regarding a team member's performance.
- Tell me about a time you worked with another team to achieve a common goal.
- Can you share a failure in your leadership journey and what you learned from it?
- How would you optimise a slow SQL query?
- Write a Python program to remove duplicates while keeping the first occurrence.
- Write a Python program to count the number of words in a given string.
- How would you model LinkedIn? What tables and connections would you include?
- Give me an example of a time when you had to make a split-second decision.
- How do you collaborate with product managers, UX teams, etc.?
- Tell me about a project you are most proud of.
- What is the relationship between the engineering manager and the tech lead?
- How do you ensure diversity within your team?
- Have you ever promoted anyone?
- How have you managed low performers?
- What is the composition of your current (or last) team, and how is your team organized?
- Have you managed other managers?
Meta Data Engineering Manager Roles and Responsibilities
Following are the roles and responsibilities of a Meta Data Engineering Manager:
- You'll work closely with Product Managers, Data Scientists, and Software Engineers to support product launches and roadmaps.
- You’ll be in charge of designing and building the data architecture that supports product launches and drives insights.
- You’ll drive the design, building, and launching of new data models and pipelines in production.
- You’ll manage the delivery of high-impact dashboards and data visualisations.
- You will use advanced technologies and handle one of the world’s largest and richest datasets.
Meta Data Engineering Manager Skills and Qualifications
Here are the skills and qualifications that a Meta Data Engineering Manager must have:
- At least 8 years of experience working with BI and Data Warehousing.
- A proven history of leading and scaling 3-person teams effectively.
- Strong expertise in data infrastructure and data architecture.
- Robust operational skills for driving efficiency and process optimization.
- Excellent project management abilities to oversee complex initiatives.
- Experience in SQL and at least one OOP language (Python, Java, etc.).
- Advanced degree is a plus.