Data Engineer 路 Intern to senior
Data engineering interviews lean heavily on SQL and Python. Expect to write queries by hand, explain how indexes and transactions work, and discuss how data is modelled and partitioned at scale.
64 questions
Explain how you identified ambiguity, clarified the important decisions, made reasonable assumptions and kept delivery moving.
Explain how you assessed urgency, impact, dependencies and risk, then communicated the resulting priorities.
Show respectful disagreement, evidence-based reasoning, willingness to listen and commitment after the final decision.
Identify a recurring source of wasted time or risk, make a focused improvement, and demonstrate its impact.
Explain the manual problem, why automation was worthwhile, how you implemented it safely and what time or error reduction resulted.
Show how you adapted your explanation to the audience, focused on the outcome and avoided unnecessary technical jargon.
Show how you noticed a risk early, validated it, communicated it and added a safeguard.
Describe how you understood the stakeholder鈥檚 goals, handled disagreement professionally and reached a workable outcome.
Explain why you accepted, reduced or paid down technical debt and how you managed the associated risk.
Start with the problem, evaluate alternatives, validate the technology with a small experiment and explain how you managed adoption and risk.
Explain how you identified what the developer needed, coached rather than simply solved the problem, and measured their progress.
Isolation levels (Read Uncommitted, Read Committed, Repeatable Read, Serializable) control how much concurrent transactions can see of each other's changes, trading consistency against concurrency to prevent dirty reads, non-repeatable reads and phantom reads.
Read the execution plan with EXPLAIN to find full scans and expensive joins, add or fix indexes for the filter and join columns, select only the columns and rows you need, and rewrite predicates so indexes can be used.
The Global Interpreter Lock (GIL) in CPython lets only one thread execute Python bytecode at a time, so threads do not speed up CPU-bound code but still help with I/O-bound work.
Threading runs concurrent threads that share memory and suit blocking I/O, multiprocessing runs separate processes to use multiple CPU cores for CPU-bound work, and asyncio runs many non-blocking tasks cooperatively on one thread for high-volume I/O.
Replication copies the same data to several servers to improve read capacity and availability, while sharding splits the data across servers so each holds only a part, which increases write capacity and total storage.
Show how you built trust, understood competing concerns, used evidence and helped the group reach a decision.
Explain the constraints, alternatives, trade-offs, decision criteria and why the chosen option was appropriate for the context.
Cover detection, communication, mitigation, investigation, recovery and the changes made afterwards to reduce recurrence.
Explain why the technology looked attractive, what problem it actually solved, and why its cost or complexity was not justified in your context.