Facebook 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 what a Data Analyst at Facebook is all about, including the required skills and qualifications, compensation, the interview process, tips, and sample data analyst interview questions.
Facebook 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 Facebook work on many different teams and are extremely cross-functional. Generally, however, data analysts at Facebook 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 Facebook
There are hundreds of teams at Facebook that you can work for (not exaggerating), but below are some examples of teams that are currently hiring data analysts right now:
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 Facebook 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 Facebook. The growth marketing and product analyst roles are similar to the data analyst roles at Facebook 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 Facebook.
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:
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 Facebook, 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.
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.
After the phone screens come the onsite interviews, which are typically composed of four 30 minute rounds:
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.
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.
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.
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 Facebook’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.
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