# 35+ Data Engineer Interview Questions & Answers (2026)

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Source: https://www.aced.io/blog/top-data-engineering-interview-questions
Source observed: 2026-11-05T00:19:21.946Z
Last checked: 2026-11-05T00:19:21.946+00:00
Indexed: 2026-11-06T01:34:42.66+00:00

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# 35+ Data Engineer Interview Questions & Answers (2026)

We publish this data-engineering interview guide for candidates preparing for SQL, pipeline design, data modeling, warehouse architecture, system design, and cross-functional collaboration interviews. It collects questions reported by candidates at Databricks, Meta, Amazon, Google, and other companies, with sample answers from our community.

Aced Team • Last updated a month ago

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We created the guide with insights from our [data engineering interview prep course](https://www.aced.io/courses/data-engineering?ref=exponent-blog.ghost.io), a community of 500+ data engineering candidates and hiring managers, [data engineer interview experiences](https://www.aced.io/experiences?role=data-engineer&ref=exponent-blog.ghost.io), and [real DE questions](https://www.aced.io/questions?role=data-engineer&ref=exponent-blog.ghost.io).

## Top data engineer interview questions

1. [Top Employees](https://www.aced.io/questions/3044/top-earning-employees?ref=exponent-blog.ghost.io) — Find the highest-paid employee in each department. **69 answers**.
2. [What's the difference between a data lakehouse and a data warehouse?](https://www.aced.io/questions/4661/lakehouse-vs-warehouse?ref=exponent-blog.ghost.io) — Asked at Databricks. **5 answers**.
3. [Employee Earnings](https://www.aced.io/questions/3960/employee-earnings?ref=exponent-blog.ghost.io) — SQL window functions and aggregation. **76 answers**.
4. [Design a document processing pipeline.](https://www.aced.io/questions/5529/design-a-document-processing-pipeline?ref=exponent-blog.ghost.io) — Asked at Databricks. **6 answers**.
5. [Design a database schema for a fitness app.](https://www.aced.io/questions/4843/fitness-app-data-schema-design?ref=exponent-blog.ghost.io) — Asked at Databricks and DoorDash. **3 answers**.
6. [What is the difference between NoSQL and SQL?](https://www.aced.io/questions/2167/difference-nosql-sql?ref=exponent-blog.ghost.io) — Asked at Amazon, Apple, and Walmart. **3 answers**.
7. [What is a Medallion Architecture?](https://www.aced.io/questions/4663/medallion-architecture?ref=exponent-blog.ghost.io) — Asked at Databricks. **2 answers**.
8. [Explain the differences between Parquet and Avro.](https://www.aced.io/questions/3390/differences-between-parquet-and-avro?ref=exponent-blog.ghost.io) — **2 answers**.
9. [Tell me about a time you made a mistake.](https://www.aced.io/questions/240/mistake?ref=exponent-blog.ghost.io) — Asked at 15+ companies including Amazon, Google, and Meta. **110 answers**.
10. [Explain wide vs. narrow dependencies in Apache Spark.](https://www.aced.io/questions/3387/differences-wide-narrow-dependencies-apache-spark?ref=exponent-blog.ghost.io) — **1 answer**.

Jump to [SQL & coding questions](https://www.aced.io/blog/top-data-engineering-interview-questions#sql-coding-questions), [data pipeline design questions](https://www.aced.io/blog/top-data-engineering-interview-questions#data-pipeline-design-questions), [data modeling questions](https://www.aced.io/blog/top-data-engineering-interview-questions#data-modeling-questions), [system design & architecture questions](https://www.aced.io/blog/top-data-engineering-interview-questions#system-design-architecture-questions), or [behavioral questions](https://www.aced.io/blog/top-data-engineering-interview-questions#behavioral-questions).

## SQL & coding questions

SQL appears in almost every data-engineering interview. Window functions, aggregation, joins, and query optimization are recurring themes.

1. [Top Earning Employees](https://www.aced.io/questions/3044/top-earning-employees?ref=exponent-blog.ghost.io) — Highest-paid employee in each department. **69 answers**.
2. [Employee Earnings](https://www.aced.io/questions/3960/employee-earnings?ref=exponent-blog.ghost.io) — Running totals and rankings with window functions. **76 answers**.
3. [Remove Duplicate Emails](https://www.aced.io/questions/3083/remove-duplicate-emails?ref=exponent-blog.ghost.io) — Delete duplicates while keeping the first entry; asked at IBM. **70 answers**.
4. [Top Salaries by Department](https://www.aced.io/questions/3045/top-salaries-by-department?ref=exponent-blog.ghost.io) — Top N salaries per group using `DENSE_RANK`; asked at Tesla. **35 answers**.
5. [Instagram Likes](https://www.aced.io/questions/3049/instagram-likes?ref=exponent-blog.ghost.io) — Engagement aggregation with `GROUP BY` and `HAVING`. **47 answers**.
6. [Monthly Post Success Analysis](https://www.aced.io/questions/3955/monthly-post-success-analysis?ref=exponent-blog.ghost.io) — Month-over-month success rates; asked at LinkedIn. **36 answers**.
7. [Calculate Test Scores](https://www.aced.io/questions/3047/calculate-test-scores?ref=exponent-blog.ghost.io) — NULL handling and conditional aggregation; asked at Google. **27 answers**.
8. [Find Customer Lifetime Value (LTV)](https://www.aced.io/questions/3975/find-customer-lifetime-value?ref=exponent-blog.ghost.io) — Join transactions with users and calculate business metrics. **7 answers**.

### Sample answer: “Top Earning Employees”

**Sample community answer:** Use `DENSE_RANK()` partitioned by department and ordered by salary descending. A CTE can rank employees within each department, followed by an outer filter for rank 1. This preserves ties, unlike approaches that do not account for multiple employees sharing the highest salary. Interviewers use the problem to assess window-function knowledge, query structure, edge-case handling, and large-dataset reasoning.

### SQL & coding interview tips

Know the differences among `RANK()`, `DENSE_RANK()`, `ROW_NUMBER()`, and `LAG/LEAD`, especially how they handle ties. Explain your joins, edge cases, and approach before writing the query. Mention NULL handling with `COALESCE`, `NULLIF`, or `IS NOT NULL`. For billions of rows, discuss partitioning, indexing, or materialized intermediate results.

[Browse data engineer questions with community answers →](https://www.aced.io/questions?role=data-engineer&ref=exponent-blog.ghost.io)

## Data pipeline design questions

These questions test reliable data movement at scale, including orchestration, fault tolerance, and batch-versus-streaming trade-offs.

1. [Design a document processing pipeline.](https://www.aced.io/questions/5529/design-a-document-processing-pipeline?ref=exponent-blog.ghost.io) — Asked at Databricks. **6 answers**.
2. [What's the difference between a data lakehouse and a data warehouse?](https://www.aced.io/questions/4661/lakehouse-vs-warehouse?ref=exponent-blog.ghost.io) — Asked at Databricks. **5 answers**.
3. [What is a Medallion Architecture?](https://www.aced.io/questions/4663/medallion-architecture?ref=exponent-blog.ghost.io) — Asked at Databricks. **2 answers**.
4. [What is Delta Lake?](https://www.aced.io/questions/4662/what-is-delta-lake?ref=exponent-blog.ghost.io) — Asked at Databricks. **1 answer**.
5. [When is Hadoop better than PySpark?](https://www.aced.io/questions/4593/hadoop-vs-pyspark?ref=exponent-blog.ghost.io) — Asked at Google. **1 answer**.
6. [How would you handle scheduling dependencies between two nightly jobs?](https://www.aced.io/questions/4666/manage-dependent-nightly-jobs?ref=exponent-blog.ghost.io) — Asked at Databricks. **1 answer**.
7. [How would you handle a task in a nightly job that fails 10% of runs?](https://www.aced.io/questions/4665/handle-nightly-job-task-failures?ref=exponent-blog.ghost.io) — Asked at Databricks.
8. [Design Netflix's Clickstream Data Pipeline.](https://www.aced.io/questions/4590/netflix-clickstream-data-pipeline?ref=exponent-blog.ghost.io)

### Sample answer: lakehouse versus warehouse

**Sample community answer:** A warehouse stores structured, cleaned data optimized for SQL analytics, using schema-on-write. A lakehouse combines flexible, lower-cost lake storage with ACID transactions, schema enforcement, and warehouse-like query performance. The sample answer identifies Databricks Delta Lake and Apache Iceberg as leading implementations, and explains that lakehouses can support both unstructured ML workloads and structured BI analytics, while traditional warehouses remain suitable for purely structured, well-modeled data.

### Data pipeline design interview tips

Discuss retries, idempotency, dead-letter queues, alerting, batch-versus-streaming, consistency-versus-availability, cost-versus-latency, and the source-to-serve data flow. Relevant tools include Airflow, Dagster, Prefect, Debezium, and Kafka Connect.

[Take the data engineering interview course →](https://www.aced.io/courses/data-engineering?ref=exponent-blog.ghost.io)

## Data modeling questions

These questions test entities, relationships, grain, normalization, query performance, and slowly changing dimensions.

1. [Design a database schema for a fitness app.](https://www.aced.io/questions/4843/fitness-app-data-schema-design?ref=exponent-blog.ghost.io) — Asked at Databricks and DoorDash. **3 answers**.
2. [Design a data warehouse schema for a ride-sharing service.](https://www.aced.io/questions/4586/ride-sharing-data-model?ref=exponent-blog.ghost.io) — **3 answers**.
3. [Design a data warehouse schema for Instagram.](https://www.aced.io/questions/4911/instagram-data-model?ref=exponent-blog.ghost.io) — **1 answer**.
4. [Design a data warehouse schema for customer support.](https://www.aced.io/questions/4587/customer-support-data-model?ref=exponent-blog.ghost.io) — **1 answer**.
5. [Design a data warehouse schema for Spotify.](https://www.aced.io/questions/4711/data-warehouse-schema-spotify?ref=exponent-blog.ghost.io)
6. [Design a data warehouse schema for Amazon.](https://www.aced.io/questions/4943/design-data-schema-amazon?ref=exponent-blog.ghost.io)

### Sample answer: fitness-app schema

**Sample community answer:** Clarify the analytics use case and define the grain first. For engagement reporting, use a star schema with `fact_workouts` at one row per workout session, plus `dim_users`, `dim_exercises`, `dim_workout_types`, and `dim_dates`. Measures can include `duration_minutes`, `calories_burned`, and `sets_completed`. Use SCD Type 2 for user attributes that change over time, with effective and expiry dates.

### Data modeling interview tips

Ask what one row represents before drawing tables. Default to a star schema for dashboard and reporting workloads unless an OLTP design is requested. Identify changing dimensions and select SCD Type 1, 2, or 3 with a reason.

[Browse data engineer questions with community answers →](https://www.aced.io/questions?role=data-engineer&ref=exponent-blog.ghost.io)

## System design & architecture questions

1. [What is the difference between NoSQL and SQL?](https://www.aced.io/questions/2167/difference-nosql-sql?ref=exponent-blog.ghost.io) — Asked at Amazon, Apple, and Walmart. **3 answers**.
2. [Explain the differences between Parquet and Avro.](https://www.aced.io/questions/3390/differences-between-parquet-and-avro?ref=exponent-blog.ghost.io) — **2 answers**.
3. [What is the difference between OLTP and OLAP?](https://www.aced.io/questions/2168/difference-oltp-olap?ref=exponent-blog.ghost.io) — Asked at Amazon. **1 answer**.
4. [Explain wide vs. narrow dependencies in Apache Spark.](https://www.aced.io/questions/3387/differences-wide-narrow-dependencies-apache-spark?ref=exponent-blog.ghost.io) — **1 answer**.
5. [What is the best way to connect SQL databases and why?](https://www.aced.io/questions/2156/best-way-connect-sql-databases?ref=exponent-blog.ghost.io) — Asked at Airbnb and Google. **2 answers**.
6. [Explain the differences between multithreading and multiprocessing.](https://www.aced.io/questions/3801/differences-multithreading-multiprocessing?ref=exponent-blog.ghost.io) — **4 answers**.
7. [What is PySpark?](https://www.aced.io/questions/5092/what-is-pyspark?ref=exponent-blog.ghost.io) — **2 answers**.

### Sample answer: NoSQL versus SQL

**Sample community answer:** SQL databases such as PostgreSQL and MySQL use fixed relational schemas, ACID transactions, and complex joins. NoSQL includes key-value, document, wide-column, and graph databases, each suited to different access patterns. Both can coexist in a pipeline: PostgreSQL can serve as an OLTP source while Kafka streams changes into Cassandra or Delta Lake for analytics.

### System design & architecture interview tips

Know the trade-offs among Parquet (columnar and read-optimized), Avro (row-based, write-optimized, and schema-evolution friendly), ORC, and JSON. Connect OLTP sources to OLAP systems through CDC. For Spark, explain shuffles, partitioning, broadcast joins, and narrow transformations.

[Take the data engineering interview course →](https://www.aced.io/courses/data-engineering?ref=exponent-blog.ghost.io)

## Behavioral questions

1. [Tell me about yourself.](https://www.aced.io/questions/104/tell-me-about-yourself?ref=exponent-blog.ghost.io) — Asked at 36+ companies. **126 answers**.
2. [Tell me about a time you made a mistake.](https://www.aced.io/questions/240/mistake?ref=exponent-blog.ghost.io) — Asked at Amazon, Google, Meta, and 12+ others. **110 answers**.
3. [Tell me about a time you disagreed with someone and how you resolved it.](https://www.aced.io/questions/1350/time-disagreed-someone-resolved?ref=exponent-blog.ghost.io) — Asked at Amazon, Apple, Google, and 5+ others. **70 answers**.
4. [What is the project you are most proud of?](https://www.aced.io/questions/1031/project-you-are-most-proud-of?ref=exponent-blog.ghost.io) — Asked at 25+ companies. **60 answers**.
5. [Tell me about a time when you improved a complex process.](https://www.aced.io/questions/1005/time-improved-complex-process?ref=exponent-blog.ghost.io) — Asked at Amazon and Google. **13 answers**.
6. [How do you encourage collaboration among cross-functional teams?](https://www.aced.io/questions/916/encourage-collaboration-cross-functional-teams?ref=exponent-blog.ghost.io) — Asked at Amazon, Anthropic, and Discord. **2 answers**.

### Sample answer: making a mistake

**Sample community answer:** A pipeline change altered event-stream deduplication and was tested only on a sample dataset. It introduced duplicate warehouse records and inflated daily active users by roughly 12% for two days. The response was to roll back, backfill affected partitions, and add source-to-destination reconciliation checks. The lesson was to use full-volume staging validation for deduplication and aggregation changes.

### Behavioral interview tips

Lead with the result, then use Situation, Task, Action, and Result. Prepare data-engineering stories about production failures, data-quality issues, stakeholder pushback, and schema migrations. Quantify outcomes such as failure rates, runtime, latency, cost, or reliability.

[Start a mock interview with AI feedback →](https://www.aced.io/practice?ref=exponent-blog.ghost.io)

## Data engineer interview frameworks

### STAR method

| Step | What to do |
|---|---|
| Situation | Set the scene in 1–2 sentences: team, system, and stakes. |
| Task | Describe your responsibility and accountable outcome. |
| Action | Explain what you did, including tools, queries, and architecture decisions. |
| Result | Quantify pipeline uptime, savings, latency reduction, or stakeholder impact. |

### Pipeline design framework

| Step | What to do |
|---|---|
| Clarify | Ask about volume, velocity, format, and downstream consumer. |
| Source | Identify APIs, databases and CDC, event streams, or file drops. |
| Ingest | Choose batch such as Airflow + S3 or streaming such as Kafka + Flink; state the trade-off. |
| Transform | Use dbt for SQL-based transformation or Spark for large-scale processing; discuss schema evolution. |
| Serve | Choose a warehouse, lakehouse, or serving layer such as Redis/API. |
| Monitor | Include data-quality checks, SLA alerting, and lineage tracking. |

## Practice data engineer interview questions

- [Browse 167+ data engineer questions with community answers →](https://www.aced.io/questions?role=data-engineer&ref=exponent-blog.ghost.io)
- [Take the data engineering interview prep course →](https://www.aced.io/courses/data-engineering?ref=exponent-blog.ghost.io)
- [Start a timed mock interview with AI feedback →](https://www.aced.io/practice?ref=exponent-blog.ghost.io)

## Data engineer interview FAQ

### What topics do data engineering interviews cover?

Typical areas are SQL and coding, pipeline design, data modeling, system design, and behavioral experience. The guide says the mix varies by company: Databricks emphasizes pipeline design, while Meta emphasizes SQL and coding.

### How should I prepare?

Start with SQL, then practice batch-versus-streaming pipeline design, orchestration, medallion and lambda architectures, and star-schema modeling. Our [data engineering interview course](https://www.aced.io/courses/data-engineering?ref=exponent-blog.ghost.io) provides a structured study plan.

### How many rounds are typical?

The guide describes four to six rounds: a recruiter screen, one or two coding/SQL screens, a system or pipeline design round, and a behavioral round. Senior roles may add a past-project deep dive. The stated process usually takes two to four weeks from first contact to offer.

### How does a data engineer interview differ from a data scientist interview?

Data engineers are tested more deeply on infrastructure, SQL optimization, pipeline architecture, data modeling, distributed systems, and production reliability. Data scientists are tested more deeply on statistics, machine learning, A/B testing, and product analytics.

### What SQL concepts should I know?

Prepare window functions, CTEs, complex joins, `GROUP BY` with `HAVING`, `CASE`, explain plans, indexes, recursive CTEs, `MERGE`/`UPSERT`, partitioning, and schema-evolution handling.

## Related resources

- [Browse data engineer interview questions](https://www.aced.io/questions?role=data-engineer&src=blog)
- [Browse Data Engineer coaches](https://www.aced.io/coaching?role=data-engineer&src=blog) — Mock interviews, career coaching, and resume review.
- [Create your free account](https://www.aced.io/signup?src=blog)

### Related courses

![Data Engineering Interview Prep Course](https://images.ctfassets.net/x78yjrjc11pq/5XoOCgXlGqN2fa1zaroNWd/9a2e23df48a212bc00d352873feb2d6a/Data_Engineer_Interview_Prep_Hero_Image.jpg?w=800&h=800)

[Data Engineering Interview Prep Course](https://www.aced.io/courses/data-engineering) — 6 courses, 3,700 students. Practice data modeling, pipeline design, SQL, coding, behavioral interviews, and more.

![Behavioral Interviews: A Practical Guide for Engineers](https://images.ctfassets.net/x78yjrjc11pq/47qcoHjzFZXE37GoacedkB/9d8e3f4910d8b06b21e76745cd9cbff3/AdobeStock_1142146335.jpeg?w=800&h=800)

[Behavioral Interviews: A Practical Guide for Engineers](https://www.aced.io/courses/eng-behavioral) — 28 lessons for senior+ behavioral interviews.

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- [Top 25 SQL Data Engineering Interview Questions](https://www.aced.io/blog/top-sql-data-engineering-interview-questions) — A month ago • 19 min read.
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