Data-engineering interviews test more than whether you can name Spark, Airflow, or a cloud warehouse. Employers commonly evaluate SQL, Python, data modeling, pipeline reliability, distributed processing, streaming, cloud judgment, security, and communication. The exact loop varies by company, seniority, and technology stack, so use these as recurring question areas—not a guarantee of what every employer asks.
For conceptual questions, define the idea, explain when it is useful, give an example, and name a trade-off. For design questions, clarify requirements, scale, latency, correctness, recovery, security, and cost. For behavioral questions, use STAR: Situation, Task, Action, Result.
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Quick preparation roadmap
- Practise SQL window functions, joins, aggregation, and query plans.
- Review Python collections, generators, file processing, testing, and error handling.
- Be able to state the grain of every important table.
- Design one batch and one streaming pipeline.
- Review Spark shuffles, skew, partitioning, and joins.
- Prepare stories about an incident, disagreement, ambiguous requirement, and measurable improvement.
How to use this guide
Say your answer aloud before reading the model points. Interviewers often follow up with questions such as: “What breaks at 10 times the volume?”, “How would you test it?”, “How would you backfill it?”, and “What metric would alert you?” Entry-level candidates should emphasize fundamentals and reasoning. Mid-level candidates should add production ownership. Senior candidates should discuss architecture, governance, cost, reliability, and organizational trade-offs.
Core concepts and architecture
1. What is data engineering?
What it tests: Whether you understand the outcome, not just the tools.
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Strong answer: Data engineering is the design, construction, operation, and improvement of systems that ingest, store, transform, and serve reliable data. The deliverable is trusted data for a business or operational use case. Throughput matters, but so do correctness, freshness, security, usability, and cost.
Common mistake: Describing it only as moving data between databases.
Follow-up: How would you measure a successful pipeline? Level: All.
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2. Describe an end-to-end pipeline you built.
Cover the source, ingestion method, raw storage, transformations, model, serving layer, orchestration, tests, monitoring, recovery, and backfill strategy. Explain what happened when the source was late or changed its schema. Quantify a result such as freshness, cost, failure rate, or analyst time saved.
3. What is the difference between ETL and ELT?
ETL transforms before loading into the destination. ELT loads raw or lightly processed data first and transforms it in the warehouse or lakehouse. ETL can be appropriate when data must be filtered or masked before landing; ELT is attractive when the destination supplies scalable compute. Cloud systems do not always require ELT.
4. Compare a data lake, warehouse, and lakehouse.
Lakes generally provide flexible storage for raw or varied data. Warehouses emphasize curated, structured analytical workloads. Lakehouses aim to combine lake storage flexibility with warehouse-style management and analytics. These are architectural patterns rather than perfectly standardized product categories. Performance and cost depend on file and table formats, partitioning, workload, governance, compute, and maintenance.
5. Batch versus streaming: what is the difference?
Batch processes data periodically; streaming processes events continuously or in short intervals. Compare latency, cost, operational complexity, ordering, replay, duplicate handling, and late data. Daily finance reporting may fit batch, while fraud alerts may require low latency.
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Ask for the maximum acceptable delay, whether the result triggers an action, event volume, source characteristics, duplicate tolerance, replay requirements, and operating budget. Do not build streaming merely because it sounds modern: continuous systems add monitoring, state, offset, and recovery complexity.
7. What is an idempotent pipeline?
Rerunning the same input should not incorrectly duplicate or corrupt the result. Techniques include deterministic partition replacement, merges on stable business keys, source identifiers, offsets, and deduplication. “Exactly once” is not automatically an end-to-end property; the source, processor, sink, and external side effects all matter.
SQL interview questions
The examples below use PostgreSQL or ANSI-like SQL. Clarify dialect, null behavior, time zone, date boundaries, duplicate handling, and tie rules before coding.
8. Find the second-highest salary.
WITH ranked AS (
SELECT salary,
DENSE_RANK() OVER (ORDER BY salary DESC) AS rnk
FROM employees
)
SELECT salary
FROM ranked
WHERE rnk = 2;DENSE_RANK returns the second distinct salary. Ask whether the interviewer instead wants the second row, in which case ROW_NUMBER may be appropriate.
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9. Find the highest-paid employee in each department.
WITH ranked AS (
SELECT e.*,
RANK() OVER (
PARTITION BY department_id
ORDER BY salary DESC
) AS rnk
FROM employees e
)
SELECT *
FROM ranked
WHERE rnk = 1;Use ROW_NUMBER with a deterministic employee-ID tie-breaker when only one employee may be returned. Use RANK when ties should remain.
10. Find duplicate records.
SELECT customer_id, email, COUNT(*) AS record_count
FROM customers
GROUP BY customer_id, email
HAVING COUNT(*) > 1;First define a duplicate: identical physical rows, or repeated values for a business key? Then define which record survives.
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11. Keep only the latest customer record.
WITH ranked AS (
SELECT t.*,
ROW_NUMBER() OVER (
PARTITION BY customer_id
ORDER BY updated_at DESC, ingestion_id DESC
) AS rn
FROM customer_updates t
)
SELECT *
FROM ranked
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12. Calculate a running total.
SELECT account_id, transaction_date, amount,
SUM(amount) OVER (
PARTITION BY account_id
ORDER BY transaction_date
ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
) AS running_total
FROM transactions;Clarify what happens when several transactions share a timestamp. Add a stable ordering column if required.
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Clarify whether “seven days” means seven rows or seven calendar days. A calendar table is often needed when dates with no events must count. A row-based window can produce a different answer when activity is sparse.
14. Find users active on consecutive days.
Deduplicate to one row per user per day, order by user and date, use LAG(activity_date), and flag a row when the date difference from the prior row is one day. Then filter the flagged rows.
15. Sessionize events with a 30-minute inactivity gap.
Order events by user and timestamp. Compare each event with LAG(event_time); flag a new session when the gap exceeds 30 minutes or there is no prior event. Cumulatively sum the flags within each user to create a session ID. Handle duplicate timestamps explicitly.
16. Calculate an action-conversion percentage.
Define the denominator first: distinct users, sessions, or events? Also specify the observation period, time zone, repeat actions, and whether the action must occur after signup. A technically correct formula can still answer the wrong business question.
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WHERE filters rows before grouping. HAVING filters groups after aggregation. Filtering non-aggregated rows in WHERE is usually clearer and can reduce the data processed.
18. Explain common joins.
INNER JOIN keeps matches; LEFT JOIN keeps every left row; RIGHT JOIN keeps every right row; FULL OUTER JOIN keeps unmatched rows from both sides. A frequent error is placing a right-table filter in WHERE after a left join, unintentionally turning it into an inner join.
19. How do indexes affect performance?
Indexes can accelerate selective lookups and joins, but add storage and write overhead. Consider selectivity, composite-column order, statistics, query plans, and whether the workload is scan-heavy. Adding an index does not always improve performance.
20. How would you optimize a slow query?
- Inspect the execution plan.
- Check join cardinality and accidental row multiplication.
- Filter appropriately and select only needed columns.
- Review indexes, clustering, partition pruning, and statistics.
- Look for repeated expensive transformations and unnecessary scans.
- Measure before and after, including data scanned and concurrency impact.
Python and programming
21. How do you process a file larger than memory?
Stream line by line, read in chunks, and use iterators or generators. Avoid converting the whole file into a list. If the transformation requires distributed computation, move it to an appropriate processing engine rather than repeatedly increasing local memory.
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A list is an ordered mutable sequence; a tuple is an ordered, commonly immutable record; a set stores unique values and supports membership operations; a dictionary maps keys to values. State expected complexity only with the workload and implementation assumptions.
23. How do you handle malformed records?
Send bad records to quarantine or dead-letter storage with structured error details. Track the malformed-record rate, decide when the job should fail, alert on abnormal levels, and support replay after correction. Do not silently discard data.
24. How would you productionize a Python ingestion script?
Externalize configuration, use managed secrets, add timeouts and exponential-backoff retries, handle pagination and rate limits, log structurally, emit metrics, checkpoint progress, make writes idempotent, package dependencies, and add unit, integration, and regression tests.
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25. How do you test data-engineering code?
Use unit tests for parsing and transformations, integration tests against representative systems, schema or contract tests, data-quality tests for nulls and uniqueness, small end-to-end fixtures, and regression tests for incidents that have already occurred.
Data modeling and warehouses
26. What is normalization, and when would you denormalize?
Normalization reduces redundancy and update anomalies. Denormalization may simplify analytical queries or improve read performance. Choose based on workload, freshness, governance, maintenance, and storage—not on a universal rule.
27. Star versus snowflake schema?
A star schema places relatively denormalized dimensions around facts and often simplifies BI queries. A snowflake schema normalizes dimensions into related tables, reducing some redundancy but adding joins. Neither is always superior.
28. Fact table versus dimension table?
Facts represent measurable events or states; dimensions provide descriptive context. Always state the grain, such as “one row per order line” or “one row per daily customer snapshot.”
29. Why is table grain important?
Grain is what one row represents. If an order table has one row per order and a line-item table has several rows per order, joining them can multiply order-level amounts. State the grain before designing joins, metrics, or tests.
30. What are slowly changing dimensions?
Type 1 overwrites history. Type 2 preserves history with effective dates and a current-row indicator. Type 3 stores limited previous values. Use Type 2 when reports must show the dimension as it existed when a fact occurred.
31. How do you handle late-arriving data?
Use watermarks, rolling-window reprocessing, correction jobs, upserts or merges, reconciliation, and explicit freshness or completeness states. Explain whether a late record revises an already published aggregate.
32. What is partitioning?
Partitioning divides data by a key such as date or region so queries can avoid irrelevant data. Watch for too many small partitions, skew, queries that omit the key, and costly updates to partition columns.
33. Why compact files?
Many small files increase metadata, scheduling, and task overhead. Compaction should be balanced against write latency, concurrent readers, file-size targets, table format, and maintenance cost.
Spark and distributed processing
34. Explain Spark drivers and executors.
The driver coordinates the application and creates the execution plan. Executors run tasks and hold data or intermediate state. Network transfer, memory pressure, skew, and serialization often dominate runtime.
35. Transformations versus actions?
Transformations build a logical computation; actions trigger execution and return or write results. Spark’s lazy evaluation means defining a transformation alone does not run the job.
36. What is a shuffle?
A shuffle redistributes data across partitions for operations such as joins, grouping, and aggregation. It incurs network and often disk I/O and is sensitive to skew, partition counts, and spill.
37. How do you diagnose a slow Spark job?
Inspect the Spark UI for expensive stages, shuffle read/write, spills, executor failures, skewed partitions, repeated actions, join strategy, partition counts, serialization, and Python UDF overhead. Change one factor and benchmark it.
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38. What is data skew?
Skew occurs when a few keys hold disproportionate data, leaving some tasks much slower than others. Possible mitigations include salting hot keys, pre-aggregation, carefully chosen broadcasting, adaptive execution, workload-based repartitioning, or separating exceptional keys.
39. When would you use a broadcast join?
When one side is genuinely small enough to replicate safely to executors, broadcasting can avoid a large shuffle. Row count alone is insufficient: consider serialized size, executor memory, and concurrent workloads. An oversized broadcast can cause failures.
40. Why can Python UDFs be slower than built-ins?
Built-in expressions can be optimized by the engine. Python UDFs may add serialization and cross-language overhead. Prefer built-ins where practical, but benchmark rather than applying an absolute rule.
Streaming and Kafka
41. What is a message key or partition key?
The key influences partition placement and therefore the scope of ordering. Kafka ordering is generally within a partition, not globally across a topic. Choose a key that supports the required ordering and distribution.
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42. What is consumer lag?
Lag is the distance between available records and what a consumer group has processed. Temporary lag during a spike differs from persistent lag caused by insufficient consumer capacity, slow transformations, or a blocked sink.
43. How do you handle duplicate events?
Use stable event IDs, idempotent writes, deduplication windows, upserts, checkpoints, and controlled offset management. State the retention period and what happens when an event arrives outside the deduplication window.
44. How do you handle out-of-order and late events?
Distinguish event time from processing time. Use watermarks and allowed lateness, revise previously published aggregates where necessary, and schedule reconciliation or correction jobs.
45. What does exactly-once mean?
Define the boundary: producer to broker, broker to processor, processor to sink, or end-to-end business result. A processor may avoid duplicate internal work while an external side effect still occurs twice unless the output is transactional or idempotent.
Airflow, orchestration, and operations
46. What is an Airflow DAG?
A DAG is a directed acyclic graph of tasks and dependencies. It defines execution order, scheduling, retries, timeouts, and reruns; it does not necessarily perform the transformation itself. Work may run in SQL, Python, Spark, a warehouse, or another service. See the official DAG documentation.
47. Operator versus sensor?
Operators are task templates; sensors are specialized tasks that wait for an external event. Long-running waits can consume resources, so reschedule or deferrable approaches may be preferable where supported. See Airflow task documentation.
48. How do you make a pipeline safe to rerun?
Use deterministic run identifiers, idempotent writes, partition replacement, merge keys, atomic publication, staging tables, and explicit handling for partial output. A retry should not create a second copy of the same business data.
49. How do you backfill historical data?
- Define the affected date or key range.
- Estimate compute and downstream impact.
- Separate the run from normal schedules.
- Make the job partition-aware and idempotent.
- Validate counts, freshness, and business metrics.
- Publish or swap results atomically.
- Document the change and monitor downstream effects.
50. What do you do when a production pipeline fails?
Establish impact and scope, identify the first failing task, check dependencies and recent code, schema, credential, or infrastructure changes, then decide whether to retry, roll back, or stop downstream publication. Communicate status, preserve evidence, recover safely, and add a preventive test or alert. Airflow supports local DAG testing with dag.test(); see the debugging documentation.
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51. What data-quality checks would you add?
Check completeness, validity, uniqueness, referential consistency, freshness, volume, and distribution. A job can technically succeed while publishing stale or incomplete data, so monitor business-level signals as well as task status.
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52. What is a data contract?
A contract records schema, field meaning, nullability, valid ranges, ownership, freshness, change policy, and deprecation rules between producers and consumers. Contract tests catch breaking changes before publication.
53. How do you handle schema evolution?
Use compatible changes where possible, versioned schemas, nullable additions, consumer impact analysis, contract testing, safe deprecation, and quarantine for incompatible records. A renamed field can break dashboards even when the ingestion job succeeds.
54. How do you manage secrets?
Use a managed secret store or platform identity, never hardcode credentials, restrict access by role, rotate credentials, audit access, and prevent secrets from appearing in logs. The required controls vary by platform and organization.
55. How do you protect sensitive data?
Classify data, apply least privilege, encrypt in transit and at rest, mask or tokenize sensitive fields, use row- and column-level controls where appropriate, limit retention, audit access, and keep production data out of uncontrolled non-production environments. Legal requirements depend on jurisdiction and data type.
Behavioral and project questions
56. Tell me about yourself.
Give a 60–90 second answer: current role or training, relevant experience, one technically meaningful project, measurable result, and why this role. Avoid reciting every tool you have touched.
57. Describe a difficult data problem you solved.
Explain the ambiguity, constraints, diagnosis, your contribution, design decision, measurable result, and what changed afterward.
58. Tell me about a production incident.
Use STAR and include impact, detection, containment, root cause, communication, recovery, and preventive action. Distinguish your personal contribution from the team’s work.
59. Tell me about a stakeholder disagreement.
Show that you listened, clarified the objective, used evidence, explained trade-offs, and reached a decision. The strongest answer is not necessarily one where you “won.”
60. What would you improve if you rebuilt a pipeline?
Discuss better contracts, targeted tests, simpler dependencies, partitioning, observability, cost, ownership, or safer backfills. Explain why the change matters rather than listing fashionable technologies.
61. Explain a technical project to a nontechnical stakeholder.
Start with the business problem, affected user, delivered result, and limitation or risk. Add implementation details only after establishing why the project mattered.
62. Why should we hire you?
Connect evidence of execution, ability to reason about reliability and trade-offs, and communication and ownership. A tool inventory is weaker than a concise example of delivering trusted data under constraints.
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- SQL drill: Complete one window-function, join, deduplication, and date problem under a time limit.
- Design drill: Spend 15 minutes designing a batch pipeline, then explain replay, quality, security, and cost.
- Streaming drill: Explain duplicates, late events, watermarks, lag, and sink idempotency.
- Project drill: Give a two-minute explanation with a measurable result.
- Incident drill: Prepare one STAR story covering detection, containment, recovery, and prevention.
- Stack mapping: Translate your experience into the employer’s tools without pretending the tools are interchangeable.
Questions to ask the interviewer
- What are the team’s most important data products and their freshness or reliability targets?
- How are data quality incidents detected, owned, and communicated?
- What does the current platform look like, and which parts are being changed?
- How do you handle schema changes, backfills, and pipeline ownership?
- How is success measured during the first six months?
Current product details change quickly. For example, Apache Airflow’s documentation identifies its current stable documentation version and explains DAGs, tasks, retries, and debugging; check the official documentation for the version used by a target employer. Databricks likewise documents data-engineering workflows across major cloud providers and its 2026 Associate exam guide emphasizes Spark SQL and PySpark. Product knowledge helps, but transferable reasoning is what lets you adapt to a different stack.
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