Shopee Data Engineer interviews reported by three candidates combine an HR screen, SQL and Python coding, and technical discussions on Spark, Kafka, Flink and data warehouse design, plus a behavioral or hiring manager round.
$123K
Avg. Base Comp
$150K
Avg. Total Comp
3 rounds
Typical Rounds
Not reported
Process Length
Candidates describe Shopee's Data Engineer interview as a mix of coding and applied data engineering, and the three reports differ in format, so prepare for more than one shape. One candidate had an HR phone screen, an online Python and SQL test, and a hiring manager conversation. Another reported a video-recorded SQL assessment with algorithmic problems, including a second-highest-salary query, followed by a behavioral interview. A third described a longer sequence with a technical-fit call, a system-design discussion, and a behavioral and leadership conversation.
The technical content leans practical. One candidate was asked to describe a real automation project, explain tools used in real-time and offline batch processing, and discuss how Spark, Kafka and Flink work, including how to handle Spark skew. Another was asked about optimizing queries in Snowflake-style warehouses and designing data lake and ELT pipelines. A third was asked to draw a production architecture and discuss state management, failover, late-arriving data, checkpointing, partitioning, and memory-constrained optimization.
One candidate noted each assessment question came with only one test case, so check edge cases yourself before submitting. Be ready to explain the technical decisions behind your past projects. Confirm the interview focus with the recruiter in writing, because one candidate said the guidance they received was inaccurate.
Synthesized from 3 candidate reports by our editorial team.
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Real interview reports from people who went through the Shopee process.
The candidate described three stages: a 45-minute recruiter and technical-fit conversation, a 90-minute technical and system-design discussion, and a 60-minute behavioral and engineering-leadership conversation. They reported being asked to draw and discuss a complex production architecture, including scaling behavior, high-throughput pipelines, state management, failover, out-of-order and late-arriving data, and memory-pressure trade-offs. The candidate also described discussing Spark versus Flink, stream-stream joins, checkpointing, partitioning, and an algorithm optimization problem under memory constraints.
Prep tip from this candidate
Practice explaining a production architecture from first principles, including failure modes, streaming-state trade-offs, and how you would reason about memory-constrained optimization.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Shopee
Redesign batch credit card processing to enable real-time streaming for fraud detection and reporting.
| Question | |
|---|---|
| Find Duplicate Numbers in a List | |
| Hurdles In Data Projects | |
| Promoting Instagram | |
| Categorize Sales | |
| Slow SQL Query | |
| A/B Testing a Checkout Button Change | |
| Addressing Data Quality Issues | |
| Open Source Reporting Pipeline | |
| Messenger Service Design | |
| Blob Indexing | |
| Career Jumping | |
| Youtube Recommendations | |
| Why Do You Want to Work With Us | |
| Boosting Instagram Stories | |
| Your Strengths and Weaknesses | |
| Unified Live Comments | |
| Weighted Average Sales | |
| Evaluating Revenue Decline | |
| LRU Cache 1 | |
| Influencer Metrics | |
| Empty Neighborhoods | |
| 2nd Highest Salary | |
| Rolling Bank Transactions | |
| Experiment Validity | |
| Top Three Salaries | |
| Comments Histogram | |
| Closest SAT Scores | |
| Employee Salaries | |
| Subscription Overlap |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial phone screen with HR or a recruiter that covers background fit and an overview of the role. One candidate described a longer 45-minute recruiter and technical-fit conversation. Communication quality was reported differently, so confirm the focus of later rounds in writing.
Two candidates describe a coding assessment. One saw a mix of Python and SQL with easy to medium LeetCode-style questions. Another had a video-recorded SQL assessment with algorithmic problems, such as finding the second highest salary, and only one test case per question. Check edge cases yourself.
Candidates report applied questions on Spark jobs, Kafka, Flink, Spark skew, real-time versus batch processing, Snowflake query optimization and data lake or ELT design. One candidate described a 90-minute session on drawing a production architecture, covering state, failover, late data and checkpointing.
The final stage reported by candidates is a hiring manager or behavioral conversation. One described it as a discussion of whether their background matched the team, including a real automation project, while the interviewer explained team structure and current projects. Another described a behavioral final round. Outcomes varied: one offer and one rejection, and one candidate was still waiting.
