How to Crack the Data Engineer Interview at Facebook
By: Palak Sharma
Tags: Big Data Analytics, Data Engineering, Big Data, Data Science, Facebook

Facebook is a dream company for every data engineer!

With a workforce of 52,000+, the company is renowned for its growth-based company culture, lucrative salary compensation, excellent benefits, and faster promotions.

However, getting into Facebook is a challenge. With highly skilled specialists available in the jobs market, the company is on the lookout for professionals having top-notch skills.

Can someone like a big data engineer professional get into Facebook?

Surprisingly, the interview process is not as intense as people portray it to be. Although it can be stressful up to a certain extent, it shouldn’t be tough for someone with expert coding skills. To be honest, companies like Facebook, Amazon, and Google specifically look for cultural fit.

Before moving further let us first discuss the prerequisite.

Most often people get confused between both the terms, data engineer, and big data engineer.

Here’s what you need to know. A data engineer holds the responsibility of collecting data, move it, and transform the data into pipelines which are then used by the data science team. On the other hand, a big data engineer is the person who is accountable for creating and managing the company’s big data infrastructure.

Facebook tends to seek candidates with relevant work experience and time-tested skills.

If the candidate holds expertise in big data tools, it is an added advantage to the company.

Other prerequisites include:

BS or BA in Mathematics, Computer Science, Physics, or other related technical fields.

Relevant industry experience working with MPP system and MapReduce.

Extensive hands-on experience with programming languages like Python, Java, Scala, C++, Perl.

The individual needs to have more than four years of expertise in writing dataframe APIs, custom ETL, maintenance, implementation, and writing SQL.

Deep understanding of analyzing large volumes of the dataset used for identifying inconsistencies, deliverables, and gaps.

In-depth understanding of machine learning methods, dimensional data modeling, and data architecture.

Now that we’re aware of the skills, let us get into the interview process.

The interview process

Like any other technical interview process, even the data engineer and the big data engineer professional interview follows the normal standard process.

1. Initial screening: Job roles and interview process is explained in this round

This is the first and foremost interaction you will be having with the recruiter. This nearly takes 30 minutes. During the call, the recruiter explains the type of job role and what is expected of the candidate during the process.

2. Technical round: This interview goes on for an hour. The process takes place on a phone screen which specifically involves Python, Java, and SQL coding

The second-round lasts for nearly an hour. During this round, the candidate is expected to take up the coding test using the language you’re comfortable with. The coding test takes place using “Coderpad.” Generally, the test contains near about 8–10 questions — these questions are further segregated between different programming languages — Python, Java, and SQL.

3. Onsite interview: This may take around 3 to 4 back-to-back interview rounds

The final stage includes the onsite interview wherein the candidate might need to undergo a maximum of three rounds.

The first two rounds consist is the ETL rounds, and the other rounds are behavioral and data modeling.

Most of the rounds include the technical round to test the product and technical knowledge around key operational metrics. Besides the behavioral round, the other technical round is taken to test the knowledge of the candidate.

This is how the scenario might look like during the onsite interview –

1. ETL round — this is where your expertise in coding skills are tested — programming skills in Python and Java.

2. Modeling round — a combination of Python and SQL questions will be asked during this interview. And these data modeling questions will be based on the business scenario.

3. Behavioral round — in this round, the candidate’s communication skills are analyzed, whether the candidate can express their thoughts and ideas. You can start curating your own story on how you successfully achieved your projects, what were the loopholes faced and how did you solve them.

Data engineers and big data engineers are some of the most sought after job roles in the data realm. However, if you’re looking to gain an edge in this field, a data engineering certification is an ideal choice. Professional certifications help validate your skills in the big data industry.

If you succeed in your interview, the following are some of the data engineer teams you’re likely to join.

· Data warehouse team

· Novi blockchain data engineering team

· Partnerships central systems, data, and tools team

· Facebook video distribution

· Facebook app monetization (FAM) team

Also, don’t forget to go through these domains while preparing for interviews at product-based companies like Facebook or Amazon.

· The SQL query used for solving complex problems

· Data pipeline design

· Statistics and modeling

· Big data solutions like Spark, EMR

· Building data platforms and architectures

· DB performance tuning

Cracking interview at companies like Facebook requires hard work and grit.