30+ Data Engineer Interview Questions and Answers

Data Engineering is not just about moving data from point A to point B. In 2026, data engineers are expected to design scalable, reliable, cost-efficient, and analytics-ready data systems that support real-time decision making, AI workloads, and business intelligence. Modern data engineers work at the intersection of distributed systems, cloud platforms, big data processing, and analytics and reporting. They collaborate closely with data scientists, analysts, ML engineers, and business stakeholders to ensure that data is trusted, timely, and usable.

This article covers 30+ commonly asked interview questions for a data engineer, with explanations that interviewers actually expect, and not just the textbook definitions. So read on, and be interview ready as a data engineer with the perfect answers to the most common questions.

Also read: Top 16 Interview Questions on Transformer [2026 Edition]

Learning Objectives

By the end of this article, you should be able to attempt the most commonly asked data engineer interview questions with utmost confidence. You should also be able to:

• Explain end-to-end data pipelines confidently
• Understand batch vs streaming systems
• Design data lakes, warehouses, and lakehouses
• Optimize Spark jobs for real-world workloads
• Handle schema evolution, data quality, and reliability
• Answer SQL and modeling questions with clarity

Data Engineering Interview Questions

So now that you know what you are in for, here is the list of questions (and their answers) for data engineer interviews that you definitely should prepare for.

Q1. What is Data Engineering?

Data Engineering is the practice of designing, building, and maintaining systems that ingest, store, transform, and serve data at scale.

A data engineer focuses on:

• building reliable data pipelines
• ensuring data quality and consistency
• optimizing performance and cost
• enabling analytics, reporting, and ML use cases

In short, data engineers build the foundation on which data-driven decisions are made.

Q2. Explain your end-to-end data pipeline experience.

An end-to-end data pipeline typically includes:

• Data ingestion – pulling data from sources such as databases, APIs, logs, or event streams
• Storage layer – storing raw data in a data lake or object storage
• Transformation layer – cleaning, enriching, and aggregating data (ETL/ELT)
• Serving layer – exposing data to BI tools, dashboards, or ML systems
• Monitoring & reliability – alerts, retries, and data quality checks

Interviewers look for clarity of thought, ownership, and decision-making, and not just the tools you used in your experience.

Q3. What is the difference between a Data Lake and a Data Warehouse?

A Data Lake stores raw, semi-structured, or unstructured data using a schema-on-read approach.
It is flexible and cost-effective, suitable for exploratory analysis and ML workloads.

A Data Warehouse stores structured, curated data using a schema on write. It is optimized for analytics, reporting, and business intelligence.

Many modern systems adopt a lakehouse architecture, combining both. For example, raw clickstream and log data is stored in a data lake for exploration and machine learning use cases. Business reporting data is transformed and loaded into a data warehouse to support dashboards.

Q4. What are batch and streaming pipelines?

Batch pipelines process data in chunks at scheduled intervals (hourly, daily). They are cost-efficient and suitable for reporting and historical analysis.

Streaming pipelines process data continuously in near real time. They are used for use cases like fraud detection, monitoring, and live dashboards.

Choosing between them depends on latency requirements and business needs. For instance, daily sales reports can be generated using batch pipelines, while real-time user activity metrics are computed using streaming pipelines to power live dashboards.

Also read: All About Data Pipeline and Its Components

Q5. What is data partitioning, and why is it important?

Partitioning divides large datasets into smaller chunks based on a key such as:

• date
• region
• customer ID

Partitioning improves:

• query performance
• parallel processing
• cost efficiency

Poor partitioning can severely degrade system performance. Hence its important to partition data optimally to scan only relevant files, reducing query time and compute cost significantly.

Q6. How do you handle schema evolution in data pipelines?

Schema evolution is managed by:

• adding nullable fields
• maintaining backward compatibility
• versioning schemas
• using schema registries

Formats like Avro and Parquet support schema evolution better than raw JSON.

Q7. What are OLTP and OLAP systems?

OLTP systems handle transactional workloads such as inserts and updates.
They prioritize low latency and data integrity.

OLAP systems handle analytical workloads such as aggregations and reporting.
They prioritize read performance over writes.

Data engineers typically move data from OLTP to OLAP systems. You may also explain what systems you have previously worked on in your projects and why. For example, user transactions are stored in an OLTP database, while aggregated metrics like daily revenue and active users are stored in an OLAP system for analytics.

Read the difference between OLTP and OLAP here.

Q8. What is Slowly Changing Dimension (SCD)?

SCDs manage changes in dimensional data over time.

Below are the Common types:

• Type 1 – overwrite old values
• Type 2 – maintain history with versioning
• Type 3 – store limited history

Type 2 is widely used for auditability and compliance.

Q9. How do you optimize Spark jobs?

Spark optimization techniques include:

• choosing the correct partition sizes
• minimizing shuffles
• caching reused datasets
• using broadcast joins for small tables
• avoiding unnecessary wide transformations

Optimization is about understanding data size and access patterns.

Q10. What are join strategies in Spark?

Common join strategies:

• Broadcast Join – when one table is small
• Sort Merge Join – for large datasets
• Shuffle Hash Join – less common, memory dependent

Choosing the wrong join can cause performance bottlenecks. So it’s important to know what type of join is used and why. The most common join is the broadcast join. When joining a small reference table with a large fact table, we used a broadcast join to avoid expensive shuffles.

Q11. How do you handle late-arriving data in streaming?

Late data is handled using:

• event time processing
• watermarks
• reprocessing windows

This ensures correctness without unbounded state growth.

Q12. What data quality checks do you implement?

Typical checks include:

• null checks
• uniqueness constraints
• range validations
• data type checks
• referential integrity
• freshness checks

Automated data quality checks are critical in production pipelines.

Q13. Kafka vs Kinesis how do you choose?

The choice depends on:

• cloud ecosystem
• operational complexity
• throughput requirements
• latency needs

Kafka offers flexibility, while managed services reduce ops overhead. In an AWS-based setup, we typically choose Kinesis due to native integration and lower operational overhead, whereas Kafka is preferred in a cloud-agnostic architecture.

Q14. What is orchestration?

Orchestration automates and manages task dependencies in data workflows.

It ensures:

• correct execution order
• retries on failure
• observability

Orchestration is essential for reliable data pipelines. It is better to know the orchestration tools you used in your projects. Popular tools include Apache Airflow (scheduling), Prefect and Dagster (data pipelines), Kubernetes (containers), Terraform (infrastructure), and n8n (workflow automation).

Q15. How do you ensure pipeline reliability?

Pipeline reliability is ensured through:

• idempotent jobs
• retries and backoff
• logging
• monitoring and alerting
• clear SLAs

Q16. Hive managed vs external tables?

Managed tables – Hive controls both metadata and data
External tables – Hive manages metadata only

External tables are preferred in shared data lake environments, especially when there are multiple teams that could access the same data without risk of accidental deletion.

Q17. Find the 2nd-highest salary in SQL.

This question tests understanding of window functions, handling duplicates, and query clarity

Sample Problem Statement

Given a table employees containing employee salary information in column salary, find the second-highest salary.
The solution should correctly handle cases where multiple employees have the same salary and avoid returning incorrect results due to duplicates.

Solution:

To solve this problem, we need to rank salaries in descending order and then select the salary that ranks second. Using a window function allows us to handle duplicate salaries cleanly and ensures correctness.

Code:

SELECT salary

FROM (

SELECT salary,

DENSE_RANK() OVER (ORDER BY salary DESC) AS salary_rank

FROM employees

) ranked_salaries

WHERE salary_rank = 2;

Interviewers care more about the correct logic and approach used than the syntax.

Q18. How do you detect duplicate records?

Duplicates can be detected using GROUP BY with HAVING, window functions, and business keys

Sample Problem Statement

In large datasets, duplicate records can lead to incorrect analytics, inflated metrics, and poor data quality. Given a table of orders with columns user_id, order_date, and created_at, identify user records that appear more than once.

Solution:

Duplicates are detected by grouping data on business relevant columns and identifying groups with more than one record.

Using GROUP BY with HAVING:

SELECT user_id, order_date, COUNT(*) AS record_count

FROM orders

GROUP BY user_id, order_date

HAVING COUNT(*) > 1;

Using Window Function:

SELECT *

FROM (

SELECT *,

ROW_NUMBER() OVER (

PARTITION BY user_id, order_date

ORDER BY created_at

) AS row_num

FROM orders

) ranked_records

WHERE row_num > 1;

The first approach identifies duplicate keys at an aggregate level. The second approach helps isolate the exact duplicate rows, which is useful for cleanup or deduplication pipelines.

Always clarify what defines a duplicate, since this varies by business logic.

Q19. What is star vs snowflake schema?

Star schema:

• denormalized dimensions
• faster queries

Snowflake schema:

• normalized dimensions
• reduced redundancy

We use star schema for reporting dashboards to improve query performance, while snowflake schema is used where storage optimization is critical.

Q20. What is ETL vs ELT?

ETL (extract transform load) transforms data before loading.
ELT (extract load transform) loads raw data first and transforms later.

Cloud data platforms commonly prefer ELT. We choose ETL when we have legacy systems, need to hide sensitive data before it reaches the data warehouse, or require complex data cleaning.

We choose ELT when we are using cloud data warehouses (e.g., Snowflake, BigQuery), need to ingest data quickly, or want to keep raw data for future analytics etc.

Read more about ETL vs ELT here.

Q21. How do you handle backfills?

Backfills are handled by:

• partition-based reprocessing
• rerunnable jobs
• impact analysis

Backfills must be safe and isolated.

Q22. How do you reduce data pipeline costs?

Cost optimization includes:

• pruning partitions
• optimizing file sizes
• choosing correct storage tiers
• minimizing compute usage

Cost awareness is increasingly important. We generally reduce costs by optimizing partition sizes, avoiding unnecessary full table scans, choosing appropriate storage tiers, and scaling compute only when needed.

Q23. How do you version data pipelines?

Versioning is handled using:

• Git
• CI/CD pipelines
• environment separation

Q24. How do you manage secrets in pipeline?

Secrets are managed using:

• secret managers
• IAM roles
• environment based access

Hardcoding credentials is a red flag. In AWS, secrets such as database credentials are stored in AWS Secret Manager and accessed securely at runtime using IAM-based permissions.

Q25. Explain a challenging data problem you solved.

A good answer includes explaining:

• problem statement
• constraints
• your contribution
• measurable impact

Storytelling matters the most here. For instance, “The main issue we had in the pipeline was delayed and inconsistent reporting of data. I redesigned the pipeline to improve data freshness, added validation checks, and reduced processing time, which improved trust in analytics.”

Q26. How do you explain your project to non-technical stakeholders?

Your primary focus should be on:

• business problem
• outcome
• value delivered

Avoid tool-heavy and technical keyword explanations at all cost You can explain the business problem first, then describe how the data solution improved decision making or reduced operational effort, without focusing on tools.

Q27. What trade-offs did you make in your design?

It is important to understand that no system is perfect. Acknowledging and showcasing trade-offs shows maturity and experience. For instance, when we choose batch processing over real-time processing to reduce complexity and cost, we accept slightly higher latency as a trade-off.

Q28. How do you handle failures in production?

You could explain the scenarios from your experience, such as:

• debugging approach
• rollback strategy used
• preventive measures

Q29. What would you improve if you rebuilt your pipeline?

Improving a data pipeline means building upon the foundations and mistakes learnt. This tests your reflection, learning mindset, and architectural understanding. You could focus on modularity, data quality checks, improvisations, storage formats, etc., for better performance.

Q30. What makes you a good data engineer

As a good data engineer, you should understand the business context, build reliable and scalable systems, anticipate failures, and communicate clearly with both technical and non-technical teams.

You should be able to:

• thinks in systems
• write reliable pipelines
• understand data deeply
• communicate clearly

Conclusion

Hope you found this article helpful! As is clear from the questions above, preparing for an interview as a data engineer requires more than just knowing tools or writing queries. It requires understanding how data systems work end-to-end, being able to reason about design decisions, and clearly explaining your approach to real-world problems.

As a data engineer, familiarizing yourself with the commonly asked interview questions and practicing structured, example-driven answers will significantly improve your chances. If you can confidently answer most of these questions, you are well on your way to cracking Data Engineering interviews in 2026.

Best of luck!