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Sample Vetting Questions

Example questions at senior and principal level for Data Engineer, with what a strong answer covers. They show the depth TeamStation AI tests. They are not a real candidate interview.

Python

1. Principal level, Mental Model. A CPU bound Python service does not get faster when you add threads. Why, and what are your real options?

   A strong answer covers: The global interpreter lock and what it limits; Processes, native extensions or another service; I/O bound work being different; Newer free threaded builds and their trade offs.

2. Principal level, Mental Model. How do you introduce type checking into a 400,000 line untyped codebase without stopping delivery?

   A strong answer covers: Start at boundaries and new code; A gradual strictness ratchet in CI; Public interfaces first; What types will not catch.

3. Principal level, Cognitive Load. A nightly pipeline now takes 9 hours and used to take 2. How do you find the cause and make it safe to rerun?

   A strong answer covers: Stage timings and data volume growth; Skew and large joins; Idempotent writes; Checkpoints and partial reruns.

4. Principal level, Cognitive Load. A Django or FastAPI service has an N+1 query problem in one endpoint and the team added caching instead. What do you say?

   A strong answer covers: Fix the query first; Caching hides but does not remove cost; Invalidation cost; A test that guards the query count.

5. Principal level, Accuracy. Coverage is 92 percent and a serious bug still shipped. What does that show, and what do you add?

   A strong answer covers: Coverage measures lines run, not behavior; Property based or mutation testing; Tests at the boundaries; Production signals.

6. Principal level, Accuracy. An AI assistant wrote a data processing script that runs fine on a sample. What do you check before it touches production data?

   A strong answer covers: Edge cases in the data; Memory and runtime at full size; Idempotency and rollback; Who can see the output.

Java

7. Principal level, Mental Model. A service has a p99 latency spike every few minutes while average CPU stays flat. How do you tell a garbage collection pause from JIT recompilation, a safepoint delay or a noisy neighbor, and what do you capture before you touch any flag?

   A strong answer covers: GC logs, JFR and a profiler before any tuning; Allocation rate and humongous objects; Safepoint time versus pause time; Container CPU throttling as a separate cause.

8. Principal level, Mental Model. Describe a race condition that passed code review and testing. How did the Java Memory Model allow it, and what did the fix change?

   A strong answer covers: Visibility versus atomicity; Happens-before edges; Why a volatile flag fixes one problem and not the other; How to test it, for example with a concurrency stress tool.

9. Staff level, Procedural Knowledge. A JPA endpoint went from 40 ms to 4 s after a schema change. Walk through your diagnosis, including what you look at before you read the code.

   A strong answer covers: SQL actually sent, and how many statements; Execution plans and changed indexes; Fetch strategy and lazy loading inside serializers; Connection pool wait time.

10. Staff level, Procedural Knowledge. A monolith has 600 Spring beans and a 14 minute startup. What do you measure first, and what would you refuse to change?

    A strong answer covers: Startup tracing before any guess; Component scanning and eager initialization; Class data sharing and ahead of time options with their limits; Behavior you will not trade for speed.

11. Principal level, Cognitive Load. Heap climbs, the service restarts every 20 minutes, and a deploy went out 3 hours ago. Give your first five actions and one thing you decline to do.

    A strong answer covers: Roll back or hold, and why; Capture a heap dump or flight recording before the next restart; Limit the blast radius; Say what you know and do not know to the people waiting.

12. Staff level, Clarity. Explain a garbage collection tradeoff to a CFO who is asking why cloud spend rose 30 percent.

    A strong answer covers: Plain words and one number; Cost of lower pauses versus cost of more memory; A choice the CFO can make; No jargon the CFO has to ask about.

Go

13. Principal level, Mental Model. A service slowly accumulates goroutines until it runs out of memory. How do you find the leak and design against it?

    A strong answer covers: Goroutine dumps and profiles; Blocked sends, receives and missing cancellation; Context propagation; Ownership of each goroutine's lifetime.

14. Principal level, Mental Model. p99 latency spikes line up with garbage collection. How do you confirm it and what do you change first?

    A strong answer covers: Runtime traces and GC logs; Allocation rate and escape analysis; Reducing garbage before tuning the collector; Memory limit settings in containers.

15. Principal level, Cognitive Load. A downstream dependency slows and your service stops answering health checks. Walk through what happens and how you design against it.

    A strong answer covers: Timeouts and context deadlines on every call; Bounded connection use; Load shedding; Health checks that mean something.

16. Principal level, Clarity. Review a pull request that adds an interface for every struct. What do you say?

    A strong answer covers: Interfaces belong to the consumer; Small interfaces; Cost in readability and testing; When an interface is justified.

17. Principal level, Cognitive Load. A consumer falls behind on one partition only. What do you check and what design change prevents it?

    A strong answer covers: Key skew; Slow or poison messages; Rebalances and commit settings; Dead letter handling.

18. Principal level, Accuracy. An AI assistant generated a concurrent worker in 300 lines. What do you check first?

    A strong answer covers: Goroutine lifetime and cancellation; Shared state; Error handling; Tests that run under the race detector.

The five Axiom Cortex axes, with one example each

• Procedural Knowledge. Walk me through profiling a Python endpoint that is slow only in production. Looks for: Order of steps; What each step proves; What you do if the profile is flat.
• Mental Model. Why does a volatile flag fix a visibility bug but not a lost update on a counter? Looks for: Visibility versus atomicity; Read, modify, write as three steps; The right tool for each case.
• Accuracy. What is the difference between a nil interface and an interface holding a nil pointer? Looks for: Correct behavior; Why the check surprises people; Says when it is unsure.
• Clarity. Explain to a finance partner why the nightly report was late twice this week. Looks for: One cause named; One decision for the listener; Plain words.
• Cognitive Load. Latency is up, a deploy just went out, a customer is escalating and the dashboard disagrees with the logs. What do you do first, second and third? Looks for: A clear order; Facts kept apart from guesses; Stays correct as new facts arrive.

Questions answered on this route