25 Data Engineer Interview Questions and Answers (2026 Guide)

Data engineering interviews can be technically demanding, and what is assessed varies widely by role, seniority, stack and employer. This guide presents 25 illustrative questions spanning SQL, Python, distributed processing, cloud platforms, and system design, with concise answers and preparation strategies. The set is a study aid, not a prediction of any particular employer's interview.

Data Engineer Interview Preparation Overview

A data engineer interview loop may include a recruiter screen, a technical phone screen, a take-home or live coding challenge, a system design round, and a behavioral interview, though the exact format varies by employer and role. Before you start applying, make sure your resume highlights pipelines, data models, cloud tools, and measurable business impact. Start from a free CareerPilotAI resume template to strengthen your resume, then run it through the ATS Resume Checker. The browser-local checker can flag selected structural signals in the submitted text; it cannot verify how a particular employer's ATS will parse, rank or evaluate a resume.

• Review core SQL: JOINs, window functions, CTEs, indexes, and query optimization.
• Practice Python for data manipulation: pandas, generators, decorators, and pipeline patterns.
• Study Apache Spark architecture and the difference between RDDs, DataFrames, and Datasets.
• Understand Delta Lake, Databricks workflows, and Unity Catalog concepts.
• Know at least one cloud platform deeply: AWS, Azure, or GCP data stacks.
• Prepare two or three strong behavioral stories using the STAR method.

Top SQL Interview Questions for Data Engineers

SQL is a core skill for data engineering, and many interviews include hands-on SQL questions, though the number and topics vary by employer. The window-function behaviour described below follows the PostgreSQL documentation.

1. How do you handle slowly changing dimensions (SCDs) in SQL?

SCDs track changes to dimension data over time. Type 1 overwrites the old value (no history). Type 2 adds a new row with effective and expiry dates, preserving full history. Type 3 adds a column for the previous value. Most production warehouses use Type 2 with columns like is_current, effective_date, and expiry_date.

2. Explain the difference between RANK(), DENSE_RANK(), and ROW_NUMBER().

All three are window functions. ROW_NUMBER() assigns a unique sequential integer to each row regardless of ties. RANK() leaves gaps after tied rows (1, 1, 3). DENSE_RANK() never leaves gaps (1, 1, 2). Use RANK() for leaderboards where gaps matter, DENSE_RANK() for percentile calculations, and ROW_NUMBER() for deduplication. This behaviour is described in the PostgreSQL window-functions documentation; other SQL engines implement the same standard semantics but may differ in edge cases.

• PostgreSQL Documentation — Window Functions

3. How would you optimize a slow query in a large warehouse?

• Check the query execution plan for full table scans or expensive sorts.
• Verify that partition pruning is working for date-filtered queries.
• Replace correlated subqueries with CTEs or window functions.
• Add or adjust clustering/sort keys in Redshift or Snowflake.
• Materialize intermediate results for repeated transformations.
• Filter early: push WHERE clauses as close to the source as possible.

4. What is a CTE and when would you use it over a subquery?

A Common Table Expression (CTE) defined with WITH improves readability and allows recursive queries. Unlike a subquery, a CTE can be referenced multiple times in the same query without repeating logic. Use CTEs for multi-step transformations; use subqueries for simple single-use filters.

5. How would you calculate a rolling 7-day average in SQL?

Use a window function with ROWS BETWEEN 6 PRECEDING AND CURRENT ROW: SELECT date, metric, AVG(metric) OVER (ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS rolling_7d_avg FROM daily_metrics. Partition by user_id or region if the average should reset per group. The ROWS BETWEEN frame syntax is documented in the PostgreSQL window-functions guide.

• PostgreSQL Documentation — Window Functions

Python Data Engineer Interview Questions

Python is widely used for data pipelines, ETL scripts, and automation in many data engineering roles.

6. How do you handle large datasets in pandas without running out of memory?

• Use chunksize in pd.read_csv() to process data in batches.
• Downcast numeric types (int64 to int32, float64 to float32) to reduce memory footprint.
• Use categorical dtype for low-cardinality string columns.
• Consider switching to polars or Dask for datasets larger than available RAM.
• Filter columns early with usecols to avoid loading unused data.

7. What is the difference between a generator and a list in Python, and when would you use a generator in a pipeline?

A list stores all values in memory at once. A generator yields values one at a time using the yield keyword, consuming far less memory. In data pipelines, generators are ideal for streaming large log files, processing database cursors in chunks, or building lazy transformation chains that do not need to materialize the full dataset.

8. How do you write a reusable ETL pipeline in Python?

Separate extract, transform, and load into distinct functions or classes. Use configuration files (YAML or environment variables) for connection strings and parameters. Add error handling with try/except blocks and structured logging. Parameterize source and target so the same pipeline can be reused for different tables or environments.

9. How do you handle API rate limits in a Python data ingestion script?

• Implement exponential backoff with jitter on 429 responses.
• Track request timestamps and sleep before the next request if approaching the limit.
• Use a token bucket or leaky bucket pattern for sustained ingestion.
• Store a checkpoint after each successful batch so the job can resume without re-fetching.

Spark and Databricks Interview Questions

Distributed processing knowledge is commonly assessed for data engineering roles. The Spark answers below follow the Apache Spark SQL, DataFrames and Datasets Guide and the Spark window-function documentation.

10. What is the difference between an RDD, a DataFrame, and a Dataset in Spark?

RDDs (Resilient Distributed Datasets) are the low-level abstraction: immutable, partitioned collections with no schema. DataFrames add a schema and allow Spark to optimize execution via the Catalyst optimizer — the Spark SQL guide describes them as the preferred API for most workloads. Datasets combine the type-safety of RDDs with the optimization of DataFrames and are only available in Scala and Java.

• Apache Spark Documentation — Spark SQL, DataFrames and Datasets Guide

11. Explain Spark's lazy evaluation model.

Spark transformations (map, filter, join, groupBy) are lazy: they build a logical plan but do not execute until an action (count, collect, write) is called. This allows Spark to optimize the entire computation graph before running, combining stages, eliminating unnecessary shuffles, and applying predicate pushdown to data sources. The Spark SQL, DataFrames and Datasets Guide describes this lazy evaluation and the Catalyst optimizer.

• Apache Spark Documentation — Spark SQL, DataFrames and Datasets Guide

12. How would you optimize a Spark job that is experiencing data skew?

• Identify the skewed key using df.groupBy("key").count().orderBy(desc("count")).
• Use salting: add a random prefix to the skewed key before joining, then aggregate to remove it.
• Enable Adaptive Query Execution (AQE) in Spark 3+, which handles skew automatically in many cases.
• Repartition the DataFrame on a more evenly distributed column before the join.
• Use broadcast joins when the smaller side fits in driver memory (< 200 MB).

13. What is Delta Lake and why is it used?

Delta Lake is an open-source storage layer built on Parquet that adds ACID transactions, schema enforcement, time travel (query previous versions), and efficient upserts (MERGE INTO) to data lakes. It is the default storage format in Databricks and is increasingly used in open lakehouses on AWS S3 and Azure ADLS.

14. How do Databricks Workflows differ from Apache Airflow?

Databricks Workflows (formerly Jobs) are natively integrated with the Databricks platform, making them a straightforward choice for orchestrating notebooks and Spark jobs within a single Databricks workspace. Apache Airflow is a platform to programmatically author, schedule and monitor workflows; its architecture overview describes it as extensible through operators for many external systems, which suits complex cross-platform pipelines. Some teams use both: Airflow for overall orchestration and Databricks Workflows for Spark-intensive sub-pipelines.

• Apache Airflow Documentation — Architecture Overview

Cloud Data Engineering Interview Questions

Cloud data engineering skills are commonly expected. Some interviews include a deep dive into a specific cloud platform, though this varies by role and employer.

15. Explain the roles of AWS Glue, S3, and Redshift in a typical AWS data pipeline.

S3 is the primary raw storage layer (data lake). AWS Glue provides serverless ETL and a Data Catalog that makes S3 data queryable with schema metadata. Redshift is a columnar data warehouse optimized for analytical queries. A common pattern: ingest raw data to S3, use Glue for cleaning and transformation, and load curated data to Redshift for BI and analytics.

16. What is the difference between AWS Glue and AWS Lambda for data processing?

Lambda is ideal for lightweight, event-driven micro-tasks (file arrival notifications, small transformations under 15 minutes). Glue is designed for large-scale, scheduled ETL jobs with the full power of Spark. Use Lambda to trigger Glue jobs on S3 events, not to replace them for heavy processing.

17. How does Azure Data Factory compare to AWS Glue?

Both are cloud-native ETL orchestration services. Azure Data Factory (ADF) uses a visual pipeline designer and natively integrates with the Azure ecosystem (Synapse, ADLS, Databricks). AWS Glue is Spark-native and deeply integrated with AWS (S3, Redshift, Athena). ADF supports more built-in connectors for SaaS sources; Glue provides more direct Spark control.

18. What strategies do you use to control costs in a cloud data platform?

• Partition data by date or region to enable partition pruning and reduce scanned bytes.
• Use lifecycle policies on S3 or ADLS to move cold data to cheaper storage tiers.
• Right-size Redshift clusters and use pause/resume for dev environments.
• Use spot instances for Spark clusters where fault tolerance allows.
• Monitor query costs in Snowflake or BigQuery with cost attribution per team.

Behavioral Questions for Data Engineers

Many interviews include behavioral questions alongside technical ones. Preparing a few strong stories for common themes can help, though the weight given to behavioral rounds varies by employer.

19. Describe a time you improved the performance of a slow data pipeline.

Use the STAR method (Situation, Task, Action, Result). Example structure: a nightly batch job was running 6 hours and causing SLA breaches (Situation). You were asked to cut it to under 90 minutes (Task). You profiled the job, found a skewed GROUP BY, applied salting, and replaced a nested correlated subquery with a CTE (Action). The job now completes in 55 minutes (Result).

20. Tell me about a time you had to work with messy or incomplete data.

A common focus is your approach to data quality: how you identify issues, communicate them to stakeholders, design validation checks, and handle edge cases without blocking downstream teams. Quantify the impact where you can.

21. How do you communicate technical decisions to non-technical stakeholders?

• Lead with the business outcome, not the technical implementation.
• Use analogies: a data catalog is like a library index for all your data assets.
• Prepare a simple diagram for architecture discussions.
• Invite questions early and often rather than presenting all at once.

Additional Common Questions

• 22. What is a data lakehouse and how does it differ from a data warehouse?
• 23. How would you design a pipeline to handle late-arriving events?
• 24. What is schema evolution and how do you handle it in production?
• 25. Describe the differences between batch and streaming pipelines and when you would choose each.

Final Interview Preparation Checklist

• Update your resume to highlight data pipelines, tools, and measurable impact — start from a free CareerPilotAI resume template for a polished draft.
• Run your resume through the ATS Resume Checker. The browser-local checker can flag selected structural signals in the submitted text; it cannot verify how a particular employer's ATS will parse, rank or evaluate a resume.
• Practice SQL window functions, CTEs, and optimization scenarios on LeetCode or StrataScratch.
• Build or review at least one end-to-end pipeline project you can walk through in detail.
• Prepare STAR stories for performance improvement, data quality incident, and stakeholder communication.
• Research the company's data stack (check job descriptions, engineering blogs, and LinkedIn posts).
• Prepare three thoughtful questions to ask each interviewer.

Data engineering roles can be competitive. Combining technical depth with clear communication and real-world project experience may help you stand out, though hiring decisions depend on the employer. Use this guide as a study checklist, not just a reading exercise — practice explaining each answer out loud until it feels natural.

Sources and update notes

The technical answers above are grounded in official documentation for the technologies cited. Technology-specific examples (PostgreSQL window functions, Spark SQL/DataFrames, Spark window functions, Apache Airflow architecture) describe those technologies and should not be taken as proof of how every data platform behaves. Actual interview topics vary by role, seniority, stack and employer, and this question set does not predict any particular employer's interview. Last updated September 1, 2026: Added official PostgreSQL, Apache Spark and Apache Airflow documentation; removed unsupported employer generalizations; and clarified ATS-checker limitations.

• PostgreSQL Documentation — Window Functions
• Apache Spark Documentation — Spark SQL, DataFrames and Datasets Guide
• Apache Spark Documentation — Window Functions
• Apache Airflow Documentation — Architecture Overview