I gave the Data Engineer Onsite interview in March 2019.

A recruiter reached out to me via LinkedIn and asked for my interest in a Data Engineer role at Facebook. As I am currently working as a Data Engineer, I thought this would be a good opportunity, so I agreed to a phone screen.

The phone screen primarily included my past work experience and relocation preference. I scheduled the phone interview for a month later to allow for thorough preparation. The recruiter provided a decent amount of materials.

The questions in other reviews largely matched my experience during the phone screen. They typically involved 5 Python programming and 5 SQL questions, with 30 minutes allocated to each. The questions were of moderate difficulty, but completing all within the time limit proved challenging. Therefore, it's advisable to practice online coding via LeetCode and HackerRank rather than solely relying on questions from Glassdoor. I was able to completely solve 4 Python questions, with one nearly completed, and 3 SQL questions completely solved, with one nearly completed. The interviewer found the solutions to the 'almost-completed' questions in both categories to be logically satisfactory.

The next day, the recruiter congratulated me on my performance and connected me with another recruiter to manage my onsite process. This recruiter explained the onsite process in great detail, including what interviewers look for in a candidate. Essentially, they seek individuals who are not only technically capable but also possess business acumen and can identify 'Key Operating Metrics' for new applications. I was again provided with ample preparation materials and scheduled the interview for the following month.

The onsite interview took an unexpected turn, which is why I am not assigning a rating.

The onsite interviews are case-study based, where you are presented with a business application (e.g., a ride-share app, online gaming, or social network app).

You are expected to first identify key metrics to assess business performance and then create a Dimensional Model for that business.

Following this, you will be given tasks requiring SQL and/or Python coding.

Asking clarifying questions is encouraged, and the entire interview is described as a 'collaborative process.' However, it appears my clarifying questions were perceived negatively by the interviewers. Despite answering all questions and engaging in productive discussions, I received a rejection email the following week.

No specific flaws were mentioned in the feedback. It stated that I was a good cultural fit with a strong background and expertise, but they had found a candidate whose experience better matched their requirements. This feedback was in stark contrast to my interview experience.

Even though the interview process was well-organized, regardless of your expertise and performance, there's always a possibility of not being selected if a better-suited candidate emerges. Therefore, interviewing here feels akin to rolling a dice, requiring luck in addition to skills for selection. Whether to undertake the lengthy and demanding preparation and interview process again to work here is an individual decision. Good luck!

Phone Interview:

Intermediate level Python and SQL questions. 5 questions each for 30 minutes.

Onsite Interview:

Cannot give specifics as I had signed an NDA, but it is a real-world case study. You will have to create a dimensional model and then write queries on a whiteboard based on the interviewer's requirements. Knowing "Key-Operating Metrics" is highly recommended.