Shopee Data Engineer candidates reported a three-stage process spanning recruiter screening, Python and SQL coding, and hiring-manager or technical discussions centered on practical distributed-data work.
$150K
Avg. Base Comp
$180K
Avg. Total Comp
3 rounds
Typical Rounds
1-2 weeks
Process Length
Shopee Data Engineer interview reports point to a practical evaluation of how you work with data systems, rather than an exclusively puzzle-driven loop. One candidate described an HR phone screen, an online coding test that mixed Python and SQL, and a final hiring-manager conversation about fit with the team. That candidate encountered easy- and medium-level LeetCode-style questions, but said the emphasis was applied work: explaining an automation project, common realtime and batch-processing technologies, Spark, Kafka, Flink, and Spark-skew optimization.
A separate candidate described three stages with a 45-minute recruiter/technical-fit conversation, a 90-minute technical and system-design session, and a 60-minute behavioral and leadership discussion. Their technical discussion went deep on high-throughput streaming architecture: pipeline design, state management, failover, late or out-of-order events, checkpointing, partitioning, and memory trade-offs. They also reported an on-the-spot algorithm optimization question under memory constraints.
Prepare two clear stories: one automation or production data project and one system design you can draw and defend. Be ready to explain why you selected an approach, what breaks at higher volume, and how you would handle failure and data-ordering edge cases. The available reports are limited, so emphasis may vary by team.
Synthesized from 2 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 | |
| Boosting Instagram Stories | |
| Why Do You Want to Work With Us | |
| Unified Live Comments | |
| Your Strengths and Weaknesses | |
| Weighted Average Sales | |
| Evaluating Revenue Decline | |
| LRU Cache 1 | |
| Influencer Metrics | |
| Empty Neighborhoods | |
| 2nd Highest Salary | |
| Rolling Bank Transactions | |
| Top Three Salaries | |
| Experiment Validity | |
| Comments Histogram | |
| Closest SAT Scores | |
| Merge Sorted Lists | |
| Subscription Overlap |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial phone or recruiter conversation focused on background alignment, role fit, and, in one account, hands-on familiarity with real-time streaming engines. Prepare a concise walkthrough of relevant production work.
One candidate reported an online test combining Python and SQL, alongside easy- and medium-level LeetCode-style questions. Practice moving between code and data-query reasoning rather than treating them as separate skills.
Candidates report practical discussion of automation projects, Spark, Kafka, Flink, batch and realtime processing, and Spark-skew optimization. Another account describes deeper probing on throughput, state, failover, late data, checkpointing, partitioning, and memory trade-offs; the exact emphasis may vary.
