35+ Data Engineer Interview Questions & Answers (2026)

Data engineer interviews cover SQL, pipeline design, data modeling, warehouse architecture, and cross-functional collaboration.

This guide collects the most common data engineer interview questions reported by real candidates at Databricks, Meta, Amazon, and Google, with sample answers from our community.

Top data engineer interview questions

These are the 10 most frequently asked data engineer interview questions, based on candidate reports and community answer volume:

1. Top Employees — Write a SQL query to find the highest-paid employee in each department. (69 answers)
2. What's the difference between a data lakehouse and a data warehouse? — Asked at Databricks. (5 answers)
3. Employee Earnings — SQL window functions and aggregation. (76 answers)
4. Design a document processing pipeline. — Asked at Databricks. (6 answers)
5. Design a database schema for a fitness app. — Asked at Databricks, DoorDash. (3 answers)
6. What is the difference between NoSQL and SQL? — Asked at Amazon, Apple, Walmart. (3 answers)
7. What is a Medallion Architecture? — Asked at Databricks. (2 answers)
8. Explain the differences between Parquet and Avro. (2 answers)
9. Tell me about a time you made a mistake. — Asked at 15+ companies including Amazon, Google, Meta. (110 answers)
10. Explain wide vs. narrow dependencies in Apache Spark. (1 answer)

Jump to a category:

• SQL & coding questions
• Data pipeline design questions
• Data modeling questions
• System design & architecture questions
• Behavioral questions

SQL & coding questions

You'll write SQL in almost every data engineering interview. Window functions, aggregation, joins, and query optimization show up the most.

1. Top Earning Employees — Write a query to find the highest-paid employee in each department. (69 answers)
2. Employee Earnings — Calculate running totals and rankings using window functions. (76 answers)
3. Remove Duplicate Emails — Delete duplicate rows while keeping the first entry. Asked at IBM. (70 answers)
4. Top Salaries by Department — Find the top N salaries per group using DENSE_RANK. Asked at Tesla. (35 answers)
5. Instagram Likes — Aggregate and analyze engagement data with GROUP BY and HAVING. (47 answers)
6. Monthly Post Success Analysis — Compute month-over-month success rates. Asked at LinkedIn. (36 answers)
7. Calculate Test Scores — Handle NULL values and conditional aggregation. Asked at Google. (27 answers)
8. Find Customer Lifetime Value (LTV) — Join transactions with user data and compute business metrics. (7 answers)

Sample answer: "Top Earning Employees"

From community answers on Exponent:

The key here is using a window function rather than a subquery. Use DENSE_RANK() partitioned by department and ordered by salary descending. This handles ties correctly: if two employees share the highest salary, both appear. Write a CTE that ranks employees within each department, then filter for rank = 1 in the outer query. Avoid MAX() with a self-join, which breaks when you need the Nth highest or when ties exist.

Interviewers use this to check whether you reach for window functions or fall back to nested subqueries. In a pipeline context, window functions also perform better on large datasets because the query optimizer can parallelize the partition step.

SQL & coding interview tips

RANK(), DENSE_RANK(), ROW_NUMBER(), and LAG/LEAD appear in the majority of DE SQL rounds. Know the difference between each and when ties matter. Talk through your approach before writing anything; interviewers care as much about your thought process (what tables you'd join, what edge cases you'd handle) as the final query.

Mention NULL handling proactively with COALESCE, NULLIF, or IS NOT NULL filters. If the table has billions of rows, bring up partitioning, indexing, or materializing intermediate results. That signals pipeline thinking, not analyst thinking.

Data pipeline design questions

Pipeline questions test how you'd move data reliably at scale. Expect orchestration, fault tolerance, and trade-offs between batch and streaming.

9. Design a document processing pipeline. — Asked at Databricks. (6 answers)
10. What's the difference between a data lakehouse and a data warehouse? — Asked at Databricks. (5 answers)
11. What is a Medallion Architecture? — Asked at Databricks. (2 answers)
12. What is Delta Lake? — Asked at Databricks. (1 answer)
13. When is Hadoop better than PySpark? — Asked at Google. (1 answer)
14. How would you handle scheduling dependencies between two nightly jobs? — Asked at Databricks. (1 answer)
15. How would you handle a task in a nightly job that fails 10% of runs? — Asked at Databricks.
16. Design Netflix's Clickstream Data Pipeline.

Sample answer: "What's the difference between a data lakehouse and a data warehouse?"

From community answers on Exponent:

A data warehouse stores structured, cleaned data optimized for SQL analytics (Snowflake, Redshift, BigQuery). Data is schema-on-write: you define the schema before loading, which makes queries fast but limits flexibility. A data lakehouse combines the cheap, flexible storage of a data lake (raw files on S3/GCS in any format) with ACID transactions, schema enforcement, and query performance you'd expect from a warehouse. Databricks Delta Lake and Apache Iceberg are the two leading implementations. The practical difference: with a warehouse you ETL data before loading; with a lakehouse you can load raw data first and transform in place, while still getting reliable, repeatable queries. Most modern stacks are converging on the lakehouse pattern because it eliminates maintaining both a lake and a warehouse separately.

The strongest answers go beyond definitions. Lakehouses work for teams that need both ML workloads (unstructured data) and BI analytics (structured queries). Traditional warehouses still make sense for teams with purely structured, well-modeled data. Say when you'd pick each.

Data pipeline design interview tips

Talk about failure modes before the interviewer asks: retries, idempotency, dead letter queues, alerting. State trade-offs explicitly (batch vs. streaming, consistency vs. availability, cost vs. latency). Interviewers want to see you reason about constraints, not just pick a tool.

Reference real orchestrators by name: Airflow, Dagster, or Prefect for scheduling; Debezium or Kafka Connect for CDC. Specificity signals hands-on experience. For open-ended design questions, sketch the data flow (source, ingestion, transform, serve) before diving into details.

Data modeling questions

Most of these ask you to design schemas for real products. Interviewers want to see you think through entities, relationships, grain, and the tension between normalization and query performance.

17. Design a database schema for a fitness app. — Asked at Databricks, DoorDash. (3 answers)
18. Design a data warehouse schema for a ride-sharing service. (3 answers)
19. Design a data warehouse schema for Instagram. (1 answer)
20. Design a data warehouse schema for customer support. (1 answer)
21. Design a data warehouse schema for Spotify.
22. Design a data warehouse schema for Amazon.

Sample answer: "Design a database schema for a fitness app"

From community answers on Exponent:

Start by clarifying the analytics use case: are we optimizing for user engagement reporting, trainer performance, or workout recommendation? Assuming engagement reporting, I'd design a star schema with a fact_workouts table at the center (grain: one row per workout session), with dimension tables for dim_users, dim_exercises, dim_workout_types, and dim_dates. The fact table holds measures like duration_minutes, calories_burned, and sets_completed. For slowly changing dimensions (a user's fitness level or subscription tier changes over time), I'd use SCD Type 2 on dim_users with effective_date and expiry_date columns so we can accurately attribute historical workouts to the user's state at the time.

This pattern applies to every modeling question: clarify the grain, identify facts vs. dimensions, and address how things change over time. Mentioning SCD types unprompted signals depth.

Data modeling interview tips

Before drawing any tables, ask: "What does one row represent?" That single decision shapes everything downstream. Default to star schema unless the interviewer specifies OLTP, since most DE schemas power dashboards and reports.

Identify which dimensions change over time (user tier, product price) and pick SCD Type 1, 2, or 3 with a reason. Interviewers expect this.

System design & architecture questions

Expect questions on storage formats, processing engines, database types, and distributed computing. These test your understanding of the tools and trade-offs underneath modern data systems.

23. What is the difference between NoSQL and SQL? — Asked at Amazon, Apple, Walmart. (3 answers)
24. Explain the differences between Parquet and Avro. (2 answers)
25. What is the difference between OLTP and OLAP? — Asked at Amazon. (1 answer)
26. Explain wide vs. narrow dependencies in Apache Spark. (1 answer)
27. What is the best way to connect SQL databases and why? — Asked at Airbnb, Google. (2 answers)
28. Explain the differences between multithreading and multiprocessing. (4 answers)
29. What is PySpark? (2 answers)

Sample answer: "What is the difference between NoSQL and SQL?"

From community answers on Exponent:

SQL databases (PostgreSQL, MySQL) are relational. They enforce a fixed schema, support ACID transactions, and excel at complex joins across normalized tables. They're the right choice when data relationships are well-defined and consistency matters more than write speed. NoSQL databases cover four broad categories: key-value stores (Redis) for caching and session data, document stores (MongoDB) for flexible nested JSON structures, wide-column stores (Cassandra) for high-throughput time-series writes across distributed clusters, and graph databases (Neo4j) for relationship-heavy queries like social networks. In a data engineering context, you'll often see both: PostgreSQL as an OLTP source, with Kafka streaming change events into a Cassandra or Delta Lake layer for analytics.

A strong answer shows how both coexist in a real pipeline. Pick the right tool for the right layer instead of declaring one superior.

System design & architecture interview tips

Know your file formats: Parquet (columnar, read-optimized, analytics), Avro (row-based, write-optimized, schema evolution), ORC (Hive ecosystem), JSON (flexible, no compression). This comes up constantly.

Don't define OLTP vs. OLAP in isolation. Explain that OLTP is your source, CDC captures changes, and OLAP is your warehouse. Show the full picture. For Spark, you don't need to know Catalyst's source code, but you should be able to explain shuffles, partitioning, broadcast joins, and why narrow transformations are preferred.

Behavioral questions

Behavioral rounds for data engineers focus on cross-functional collaboration, handling ambiguity, and debugging under pressure.

30. Tell me about yourself. — Asked at 36+ companies. (126 answers)
31. Tell me about a time you made a mistake. — Asked at Amazon, Google, Meta, and 12+ others. (110 answers)
32. Tell me about a time you disagreed with someone and how you resolved it. — Asked at Amazon, Apple, Google, and 5+ others. (70 answers)
33. What is the project you are most proud of? — Asked at 25+ companies. (60 answers)
34. Tell me about a time when you improved a complex process. — Asked at Amazon, Google. (13 answers)
35. How do you encourage collaboration among cross-functional teams? — Asked at Amazon, Anthropic, Discord. (2 answers)

Sample answer: "Tell me about a time you made a mistake"

From community answers on Exponent:

At my previous company, I deployed a pipeline change that modified the deduplication logic for our event stream. I tested it against a sample dataset but didn't validate against the full production volume. The change introduced duplicate records into our analytics warehouse, which inflated our daily active user count by roughly 12% for two days before a data analyst flagged the discrepancy. I immediately rolled back the change, ran a backfill to correct the affected partitions, and set up a reconciliation check that compares source and destination row counts after every pipeline run. The broader lesson was that sample-based testing isn't sufficient for deduplication logic; edge cases only surface at scale. I now require full-volume staging validation for any pipeline change that touches dedup or aggregation logic.

This works because it's specific to data engineering (not a generic teamwork story), names a concrete metric impact, and ends with a systemic fix. For DE behavioral questions, tie stories back to pipeline reliability, data quality, or stakeholder trust.

Behavioral interview tips

Lead with the result ("I reduced pipeline failures by 40%"), then walk through Situation, Task, Action. Generic conflict stories won't land for DE roles. Prepare stories about a pipeline that broke in production, a data quality issue you caught, pushing back on a stakeholder's data request, and a schema migration you led.

Quantify everything. "I improved the pipeline" is weak. "I cut daily job runtime from 4 hours to 45 minutes by switching from full reloads to incremental CDC" is compelling.

Data engineer interview frameworks

STAR method (for behavioral questions)

STAR gives your answers a clear structure so interviewers can follow your reasoning:

Pipeline design framework (for system design questions)

When asked to design a data pipeline from scratch, this structure keeps you from missing something:

Practice data engineer interview questions

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Data engineer interview FAQ

What topics do data engineering interviews cover?

Four to five rounds, typically: SQL and coding (queries and Python scripts), pipeline design (ETL/ELT architecture), data modeling (warehouse schemas), system design (distributed systems, Spark, Kafka), and behavioral (past experience, collaboration). The mix varies by company. Databricks leans heavily on pipeline design; Meta emphasizes SQL and coding.

How should I prepare for a data engineer interview?

Start with SQL, since it appears in nearly every round. Practice window functions, CTEs, and joins on real problems. Then move to pipeline design: batch vs. streaming trade-offs, orchestration tools (Airflow, Dagster), common architectures (medallion, lambda). For data modeling, practice designing star schemas for familiar products. For a structured study plan, see our data engineering interview course.

How many rounds are in a data engineering interview?

Four to six. A recruiter screen, one or two technical coding/SQL screens, a system design or pipeline design round, and a behavioral round. Senior roles often add a "deep dive" where you walk through a past project in detail. The full process usually takes two to four weeks from first contact to offer.

What's the difference between a data engineer and a data scientist interview?

Data engineers get tested on infrastructure: SQL optimization, pipeline architecture, data modeling, distributed systems (Spark, Kafka). Data scientists get tested on using data: statistics, machine learning, A/B testing, product analytics. There's overlap in SQL, but data engineers go deeper on system design and production reliability; data scientists go deeper on statistical methods.

What SQL concepts should I know for a data engineering interview?

At minimum: window functions (RANK, DENSE_RANK, ROW_NUMBER, LAG/LEAD), CTEs, complex joins (self-joins, anti-joins), GROUP BY with HAVING, CASE expressions, and query optimization basics (reading explain plans, understanding indexes). For senior roles, also prepare for recursive CTEs, MERGE/UPSERT statements, partitioning strategies, and writing queries that handle schema evolution gracefully.