TikTok Data Engineer Interview - First Round Experience

Position: Data Engineer https://medium.com/p/e5c9ac44131e Location: Singapore Platform: TikTok Round: Technical Interview (1st Round) I recently had my first technical interview with TikTok for a Data Engineer position, and here\u2019s how it went. Interview Format: \t\u2022\tDuration: 60...

Position: Data Engineer
https://medium.com/p/e5c9ac44131e
Location: Singapore
Platform: TikTok
Round: Technical Interview (1st Round)

I recently had my first technical interview with TikTok for a Data Engineer position, and here\u2019s how it went.

Interview Format:

\t\u2022\tDuration: 60 minutes
\t\u2022\tFocus: Project discussion, Apache Spark, and SQL-related questions.

Questions Asked:

\t1.\tIntroduction and Background:
The interviewer started by asking me about my current role and projects. I gave a brief overview of the data engineering projects I\u2019ve been working on, especially focusing on big data technologies like Apache Spark.
\t2.\tProject Discussion:
I was asked in detail about the data pipelines I had built, especially focusing on how I managed large-scale data processing using Spark. This included questions about my experience with ETL processes, and the challenges I faced while optimizing data pipelines.
\t3.\tApache Spark and Salting Question:
The tricky part of the interview came when they asked about salting in Spark. The interviewer wanted to know how I would apply salting to handle skewed data in a specific scenario:
\t\u2022\tScenario:
You have a dataset of products, users, and their interaction timestamps. Some products are very popular and receive a disproportionate amount of interactions compared to others. How would you apply salting to distribute the load evenly across Spark partitions to prevent data skew?
\t\u2022\tMy Response:
I explained the concept of salting as adding a \u201Csalt\u201D (or random value) to the key to distribute the data more evenly across partitions. Here\u2019s what I said:
\t\u2022\tI would create a new column that appends a random salt value to the product key. The idea is to divide the popular products into multiple groups, which would spread the load across different partitions.
\t\u2022\tFor example, for each product_id, I would concatenate a random number (like salt = 0, 1, 2) to the product ID to generate new salted keys (e.g., product_id_0, product_id_1, product_id_2). This would help balance the workload when doing joins or aggregations, thus mitigating the skew.
\t\u2022\tAfter the processing is complete, I would remove the salt to get back to the original product_id for further analysis.

Follow-up Questions:
\t\u2022\tHow would you determine the number of salts needed?
\t\u2022\tWhat would be the impact of salting on query performance, and how would you optimize it?

Final Thoughts:

The interview was challenging, especially the in-depth technical questions on Spark and data skewness. The question on salting required not only theoretical knowledge but also a practical approach to applying it in real-world scenarios.

Make sure to brush up on your Apache Spark tuning, SQL optimization, and project-specific challenges before this round. The interviewer was highly focused on understanding how I applied these technologies in real-world use cases.