Meta Data Analyst Interview Questions + Guide in 2024

Meta Data Analyst Interview Questions + Guide in 2024

Introduction

Meta is one of the largest social media companies in Menlo Park, with around 2.5 BILLION monthly active users. This translates into over 2.5 billion pieces of content and over 500 terabytes of data processed each day. Given such large numbers, it makes sense that it currently has approximately 45,000 employees composed of thousands of engineers, data scientists, and data analysts.

This article will take a deeper look at Meta data analyst interview questions, including the required skills and qualifications, compensation, interview process, tips, and sample data analyst interview questions.

The Data Analyst Role at Meta

Meta leverages its data to improve and optimize everything you can think of, from its products to its marketing strategies to its internal operations. Thus, data analysts at Meta work on many different teams and are extremely cross-functional. Generally, however, data analysts at Meta leverage some data to complete various projects like building visualizations and dashboards, providing analytical support, or conducting exploratory analyses.

Required Skills & Qualifications

Again, the required skills and qualifications depend on the team, but they generally follow a similar pattern:

  • 2–5+ years of quantitative analysis experience, with development experience using SQL on large data sets within distributed computing platforms such as Hive/Hadoop/Redshift or similar

  • 2–5+ years of experience developing data visualizations and actionable reporting dashboards using tools such as Tableau, Domo, or similar

  • Experience processing and analyzing data sets, interpreting them for making business decisions

  • Experience communicating the results of analyses with product and leadership teams to influence the overall strategy of the product

Data Analyst Teams at Meta

There are hundreds of teams at Meta that you can work for (not exaggerating), but below are some examples of teams that are currently hiring data analysts right now:

  • Digital Rights Operations
  • People Analytics
  • Consumer Research
  • Commerce Partnerships
  • Sustainability team
  • Legal

Data Analyst vs Data Scientist at Meta

Often, there is confusion between what a data analyst does and what a data scientist does. Data analysts analyze data to find trends and often create visual data representations to share their insights with the company. They’re typically required to know how to query data and visualize data with some tool, like Tableau.

Data scientists at Meta also do what a data analyst does but more. Data scientists require a greater arsenal of skills and knowledge, including computer science, mathematics, and statistics, and generally take on more complex projects that cover things like machine learning modeling, data wrangling, and more.

Additionally, there are two more data-focused analysis roles at Meta. The growth marketing and product analyst roles are similar to the data analyst roles at Meta but are more team-specific in growth and product metrics, respectively. Many candidates will realize that the data science role is much more business-facing than they would like.

It’s common that when interviewing for the data analyst role, a recruiter may transfer you to another recruiter on the product analyst, data scientist, or growth marketing analyst teams if your skillset is better suited for those roles. A candidate can also interview for multiple roles at once at Meta.

Meta Data Analyst Interview Process

The data analyst interview process generally takes 2–3 weeks but can sometimes last over a month. Typically there are 2 main parts to the process:

1. Initial Phone Screens

There are typically two initial phone screens, each taking around 30-45 minutes.

A. Phone screen with Recruiter

The first phone screen is usually led by a recruiter and is usually conducted so that the interviewee understands the role and the team and the interviewer gets a better understanding of the interviewee. Typically the recruiter will ask about your past experiences and why Meta, and they may rarely ask a couple of technical SQL interview questions.

The recruiter is simply looking to see that you have a genuine interest in the company, that you’re a good communicator, and that there are no blatant red flags.

B. Phone screen with Hiring Manager

The second phone screen is conducted by the hiring manager and will also ask about your experiences and give you scenario-based questions. See our guide to data analyst behavioral questions.

Example Questions:

Tell me about a time you started analysis with certain expectations, and then got unexpected results Tell us about a project that you’ve managed and describe it from beginning to end.

2. Onsite Interview

After the phone screens come the onsite interviews, which are typically composed of four 30 minute rounds:

A. Technical Round: SQL

The SQL part of the technical round is usually a paired coding exercise — you should expect the interviewer to give you some data tables and problems to solve.

B. Technical Round: Analytical Study

The second round, which is also a technical interview, is a data analytics study. This is an open-ended data-related case that the interviewer will walk you through. You’re required to analyze the case, make a hypothesis, and validate it. The case can touch on a number of things like data modeling, business metrics, and dashboard reporting.

C-D. Testing for “Fit”

For the last two rounds, you’ll be asked a round of behavioral and situational interview questions to check your work style, personality, and attitude. They want to make sure that you’ll integrate well with the associated given and the company’s culture overall.

Tips to be successful

  • Make sure you ask clarifying questions, especially in the analytical case study. It’s common for interviewers not to provide every piece of information that you’ll need to be successful. They want to see that you’re thinking logically and asking the right questions.

  • If there are missing gaps in the information provided and the interviewer doesn’t give more, make sure you state your assumptions.

  • Speak your thoughts — explain your thought process so that you can demonstrate your way of thinking. In the same way that you needed to show your work in high school math, showing your thought process is equally as important as getting the right answer.

  • Getting an understanding of Meta’s culture and five core values. Throughout the interview process, they’ll look to see that you’ve demonstrated these values in your past experiences.

  • Data analyst interview questions also cover topics such as excel and data visualization, so you should also prepare if it is mentioned in the job description.

Meta Data Analyst Interview Questions

  1. Would you use UNION or UNION ALL if there were no duplicates?
  2. Does creating a view require storage in a database?
  3. Give me an example of when you worked with a large database and were able to get insights from it?
  4. How can you detect the drop of users in Instagram stories?
  5. How would you measure the success of Meta Events? Can you propose a plan to monetize from it?
  6. How would you write a query to get the top 3 highest employee salaries by department?
  7. How would you write a query to get the percentage of comments that occurs in the feed versus mentions sections of the app?

See more Meta Data Analyst interview questions from Interview Query:

Question
Topics
Difficulty
Ask Chance
Product Metrics
Medium
Very High
Python
Hard
Medium

View all Meta Data Analyst questions

Meta Data Analyst Salary

$136,432

Average Base Salary

$109,275

Average Total Compensation

Min: $95K
Max: $183K
Base Salary
Median: $135K
Mean (Average): $136K
Data points: 73
Min: $17K
Max: $215K
Total Compensation
Median: $105K
Mean (Average): $109K
Data points: 9

View the full Data Analyst at Meta salary guide

Meta Data Analyst Jobs

👉 Reach 100K+ data scientists and engineers on the #1 data science job board.
Submit a Job
Data Analyst Product Regulatory Ops
Data Analyst Product Regulatory Ops
Data Analyst Product Regulatory Ops
Data Analyst Product Regulatory Ops
Data Analyst People Data Solutions
Data Analyst Product Regulatory Ops
Data Analyst Product Regulatory Ops
Business Analyst Producer
Data Scientist Product Analytics
Product Manager