Recently helped a trainee pass the TikTok The interview was held in the Bay Area, USA. Throughout the process, his biggest feeling is: the topic is simpler than expected, but also very close to TikTok's business scenarios, and it can be seen that the company attaches more importance to whether the candidate really understands the ability of large-scale data processing, data modeling, and the combination with the business.
Compared with many companies that prefer to examine complex algorithmic questions, TikTok DE interview style is more inclined to engineering practice. In the VO interview, we provided voice assistance and thought process for the student, and his answers were very smooth, and the interviewer gave feedback in the end that "the answers were logical and clear, and the thought process was like someone who has done related systems".
I'll give you a complete breakdown of the process, questions, answer ideas, and details of our assists below.
I. Tiktok DE interview process: accidentally skipped OA
According to the process in HR's email, the original plan was OA (online written test) + three rounds of VO interviews. But in reality, because of the recruitment scheduling problem, this participant directly skipped the OA and went to the three rounds of VO. This situation is not uncommon in TikTok, especially for the data engineer position with a more suitable background, sometimes it will be exempted from the written test, and go directly to the interview.
The three rounds of interviews are scheduled as follows:
- First round: Hiring Manager Technical interview
- Behavioral problems (BQ): deep dive into past projects, especially Big Data and Data Warehouse experience; SQL questions: two questions, one on handwritten SQL output results and one on Hive script debugging; rhetorical questions session: about team, data scale, tech stack, etc.
The SQL part is not too difficult, it mainly examines whether candidates can reason logically to find out the execution order.The common error points in Hive questions are mismatch of field types, wrongly written partition fields, and sloppy syntax. In VO, we reminded the students to explain the SQL in a fixed order:
FROM/JOIN → WHERE → GROUP BY → HAVING → ORDER BY, avoiding jumps in thought. - Round 2: Easy Chat Round
This round was surprisingly easy with almost no technical questions. The interviewer mainly talked about project experience, communication style, cross-team collaboration and career planning. The participant had prepared SQL and pipeline design, but it turned out to be like a coffee chat, in fact, the focus of this round was to confirm whether the candidate could fit into the team atmosphere. - Round 3: Data Modeling (Data Modeling)
This is the session that is closest to the actual work. The interviewer gave a business scenario: the tracking of short video playback and interaction metrics. Ask the participants:- Design table structures (Fact Tables / Dimension Tables); describe fields and granularity; explain scalability.
The interviewer even opened the HackerRank link, but in the end did not let him write SQL, but focused on schema design and logic. We remind the trainees during VO assistance that the order of answers must be Business Scenario → Fact Table → Dimension Table → Extensibility, so that the sense of structure is very clear.
II. Exclusive question sharing
Although the overall difficulty is not high, the questions cover the three core directions of TikTok: large-scale data processing, recommendation system, and video storage architecture. Below is a summary of some of the questions and key points.
1. Big Data Processing
Q1: How would you design a pipeline to process 100 billion video view events per day?
- Data ingestion: Kafka
- Real-time processing: Flink / Spark Streaming
- Steps: cleaning invalid events → transformation (geo enrichment) → aggregation by user/video/region
- Storage: ClickHouse / Druid for fast queries
- Key points: exactly-once semantics, fault tolerance, scalability
Q2: How to detect trending videos in real time?
- Define trending: growth rate of views, likes, shares
- Sliding windows (5 min, 15 min, 1h)
- Flink window aggregation
- Store results in Redis for Top N queries
Q3: How do you handle Spark data skew?
- Salting hot keys
- Adaptive Query Execution (AQE)
- Two-stage aggregation
Q4: How to model user behavior in a data warehouse?
- Fact tables: video_views, likes, comments
- Dimension tables: dim_user, dim_video, dim_time, dim_location
- Consider granularity & slowly changing dimensions (SCD)
Q5: SQL optimization techniques?
- Use
EXPLAINto analyze query plan - Indexing, join optimization, early filtering, avoid full scans
2. Real-time Recommendation System
Q6: Design a real-time recommendation pipeline.
- Event stream: clicks, watch time, swipes → Kafka
- Real-time feature generation: Flink (user embedding, video popularity)
- Feature storage: Redis/Tair
- Model inference service → recommendation list
- Online evaluation: A/B testing
Q7: What metrics do you use to evaluate recommendation models?
- Offline: AUC, LogLoss
- Online: CTR, CVR, watch time, diversity, novelty
- Implementation: Kafka → Flink → dashboard
3. Video Data Architecture
Q11: How to store trillions of videos efficiently?
- Video files: S3 / Ceph (object storage)
- Metadata: Cassandra / HBase
- Playback acceleration: CDN caching
- Consider cost, latency, reliability, scalability
Q12: How to detect and remove duplicate or near-duplicate videos?
- Exact: MD5/SHA-256 hash
- Near-duplicate: perceptual hash (pHash, dHash), Hamming distance
- Run async during upload pipeline
3. SQL questions
Question : We need to calculate the number of new and old users logged in each day based on the data in the user login table fact_log. Where:
- If a user is first time logging in (i.e., the earliest login date for that user is the same day), they are considered a new user.
- Otherwise, it is considered an old user.
Solutions
In order to calculate the number of new and old users for each day, we need to:
- Determine the first login date for each user: Find the earliest login date for each user by using the
user_idgrouping. - Mark each day whether the user is a new or old user: correlate the original login table with the user's first login date to determine if the day is the user's first login.
- Count new and old users grouped by date: Based on the tagged results, count the number of new and old users per day, grouped by date.
SELECT
login_date,
user_type, COUNT(DISTINCT user_id) AS user_count
COUNT(DISTINCT user_id) AS user_count
FROM
(SELECT
l.user_id, l.login_date, l.user_count FROM (SELECT
l.login_date, l.user_id, l.login_date, l.login_date
CASE
WHEN l.login_date = f.first_login_date THEN 'new user'
ELSE 'Old user'
END AS user_type
FROM
fact_log l
JOIN
(SELECT
user_id, MIN(login_date) AS first_login_date
MIN(login_date) AS first_login_date
FROM
fact_log
GROUP BY
user_id) f
l.user_id = f.user_id) t
l.user_id = f.user_id) t
GROUP BY
login_date, user_type
ORDER BY
login_date, user_typeFollow up: In table fact_log(user_id, login_date), multiple logins on the same day are counted only once, find the longest consecutive login days in the history of each user. strong> (can't be broken in the middle, if it's broken, it's counted again).
III. Summary of interviews
As a whole, there are a few notable features of this TikTok DE interview:
- The difficulty is on the watery side: Hardly any complex algorithm questions, more scenario and architecture questions;
- Exam point clarification: Big data processing, recommender systems, data warehouse modeling;
- Business Fit: All topics are highly relevant to TikTok's short video business;
- pragmatic in style: More value is placed on the ability to get engineering on the ground than on paper.
The final feedback from the trainees was:
"During the VO process, with your voice reminders, I was able to quickly grasp the key points of my answers, and the interviewer was nodding throughout."
Overall, TikTok's DE interview is more like a discussion of "business scenarios + data engineering practices" rather than a problem-solving competition. If you can answer from the path of business logic → technical architecture → scalability, you can basically get it.
More than preparation, it's a guarantee for the real world
If you're also preparing for a data engineer interview at TikTok or any other big player, you don't have to do it alone. We Programhelp can help you:
- OA Full ghostwriting to ensure 100% pass the test;
- VO real-time voice assistance to help you answer the highlights at critical moments;
- Interview frameworks are organized so that you are no longer nervous and your answers are clear.
It is not difficult to get a big factory offer easily.
