Data Engineering · T-SQL

T-SQL Interview Questions for Data Engineering (2026 Guide)

9 min read3 easy · 6 medium · 3 hardLast updated: 22 Apr 2026

T-SQL shows up in nearly every Data Engineering interview loop. The 12 questions below cover the most frequent patterns — each with a worked example, common mistakes panels flag, and a follow-up probe. Practise them out loud, then run an adaptive drill with the AI coach.

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Top interview questions

Q1.What T-SQL questions are most common in interviewers probe depth on pipelines, sql performance, and cloud warehouse internals
easy
Interviewers probe depth on pipelines, SQL performance, and cloud warehouse internals. Start with the fundamentals of T-SQL, then move to scenario questions that test depth.
Example
Real pipeline: Kafka → bronze (Delta) → silver (schema-validated) → gold (aggregated). Idempotency at each layer.
Common mistakes
- Ignoring skew — one hot key balloons executors while the rest idle.
- Benchmarking on cold cache — production hits warm cache and the numbers invert.
Follow-up: What breaks first if the job runs on half the cluster?
Q2.How do I prepare for a T-SQL round in 2026?
medium
Time-box 30-minute practice blocks on SQL windowing, ETL design, and data modeling. Focus the first week on fundamentals, the second on realistic scenarios, and the third on mock interviews.
Example
dbt example: `{{ incremental() }}` with `unique_key=[user_id, event_id]` reliably dedupes replayed CDC events.
Common mistakes
- Benchmarking on cold cache — production hits warm cache and the numbers invert.
- Ignoring skew — one hot key balloons executors while the rest idle.
Follow-up: How do you detect and recover from duplicate writes in production?
Q3.Which T-SQL topics do interviewers weight most?
medium
Expect the top 20% of concepts in T-SQL to drive 80% of questions — prioritise those ruthlessly.
Example
Imagine a 2 TB Spark job: setting `spark.sql.shuffle.partitions=400` and broadcasting a 10 MB dim table cut runtime from 45m to 6m.
Common mistakes
- Ignoring skew — one hot key balloons executors while the rest idle.
- Benchmarking on cold cache — production hits warm cache and the numbers invert.
Follow-up: Walk me through the observability you would add before shipping this.
Q4.What's the expected bar for T-SQL at a senior level?
hard
At senior bars, interviewers expect you to design, critique, and trade off T-SQL solutions without prompting.
Example
Real pipeline: Kafka → bronze (Delta) → silver (schema-validated) → gold (aggregated). Idempotency at each layer.
Common mistakes
- Benchmarking on cold cache — production hits warm cache and the numbers invert.
- Ignoring skew — one hot key balloons executors while the rest idle.
Follow-up: Where does your solution fail if data arrives out of order?
Q5.How do I structure my answer to a T-SQL problem?
easy
Restate the problem, outline your approach, articulate trade-offs, then execute. Candidates who explain partitioning, idempotency, and schema evolution stand out.
Example
dbt example: `{{ incremental() }}` with `unique_key=[user_id, event_id]` reliably dedupes replayed CDC events.
Common mistakes
- Ignoring skew — one hot key balloons executors while the rest idle.
- Benchmarking on cold cache — production hits warm cache and the numbers invert.
Follow-up: If latency had to drop 10x, what would you change first?
Q6.What are common mistakes in T-SQL interviews?
medium
Jumping to code/model without clarifying constraints, missing edge cases, and poor communication top the list.
Example
Imagine a 2 TB Spark job: setting `spark.sql.shuffle.partitions=400` and broadcasting a 10 MB dim table cut runtime from 45m to 6m.
Common mistakes
- Benchmarking on cold cache — production hits warm cache and the numbers invert.
- Ignoring skew — one hot key balloons executors while the rest idle.
Follow-up: How would the answer change if the table was 100x larger?
Q7.Can I practice T-SQL with AI mock interviews?
medium
Yes — an adaptive coach can generate unlimited T-SQL drills tuned to your weak spots and grade responses in real time.
Example
Real pipeline: Kafka → bronze (Delta) → silver (schema-validated) → gold (aggregated). Idempotency at each layer.
Common mistakes
- Ignoring skew — one hot key balloons executors while the rest idle.
- Benchmarking on cold cache — production hits warm cache and the numbers invert.
Follow-up: What breaks first if the job runs on half the cluster?
Q8.How long should I spend preparing T-SQL?
hard
Two focused weeks for a strong professional; longer if T-SQL is new. Quality of drills beats raw hours.
Example
dbt example: `{{ incremental() }}` with `unique_key=[user_id, event_id]` reliably dedupes replayed CDC events.
Common mistakes
- Benchmarking on cold cache — production hits warm cache and the numbers invert.
- Ignoring skew — one hot key balloons executors while the rest idle.
Follow-up: How do you detect and recover from duplicate writes in production?
Q9.What's the difference between junior and senior T-SQL questions?
easy
Junior rounds test recall; senior rounds test judgement, prioritisation, and ability to reason under ambiguity.
Example
Imagine a 2 TB Spark job: setting `spark.sql.shuffle.partitions=400` and broadcasting a 10 MB dim table cut runtime from 45m to 6m.
Common mistakes
- Ignoring skew — one hot key balloons executors while the rest idle.
- Benchmarking on cold cache — production hits warm cache and the numbers invert.
Follow-up: Walk me through the observability you would add before shipping this.
Q10.Are T-SQL questions the same across companies?
medium
Core fundamentals overlap; flavour differs — top-tier companies emphasise systems thinking and trade-offs.
Example
Real pipeline: Kafka → bronze (Delta) → silver (schema-validated) → gold (aggregated). Idempotency at each layer.
Common mistakes
- Benchmarking on cold cache — production hits warm cache and the numbers invert.
- Ignoring skew — one hot key balloons executors while the rest idle.
Follow-up: Where does your solution fail if data arrives out of order?
Q11.How do I recover after a weak T-SQL answer?
medium
Acknowledge briefly, show learning mindset, and anchor the next answer in a strong framework.
Example
dbt example: `{{ incremental() }}` with `unique_key=[user_id, event_id]` reliably dedupes replayed CDC events.
Common mistakes
- Ignoring skew — one hot key balloons executors while the rest idle.
- Benchmarking on cold cache — production hits warm cache and the numbers invert.
Follow-up: If latency had to drop 10x, what would you change first?
Q12.What resources help for T-SQL interviews?
hard
Structured drills + targeted mocks + outcome tracking outperform passive reading. Expect stacked rounds covering SQL, Python/Spark, system design, and behavioral.
Example
Imagine a 2 TB Spark job: setting `spark.sql.shuffle.partitions=400` and broadcasting a 10 MB dim table cut runtime from 45m to 6m.
Common mistakes
- Benchmarking on cold cache — production hits warm cache and the numbers invert.
- Ignoring skew — one hot key balloons executors while the rest idle.
Follow-up: How would the answer change if the table was 100x larger?

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