Top Data Engineer Technical Interview Questions You Must Know

Amir Bormand · Nov 23, 2025

Data engineer interviews focus heavily on your technical skills, problem-solving mindset, and ability to work with large-scale data systems. Hiring managers want to see if you can design efficient pipelines, write reliable code, and work with distributed systems—all while maintaining clean and usable data. Every question is designed to assess your understanding of real-world data challenges.

You’ll be asked about SQL queries, Python data processing, cloud tools, pipeline architecture, and system design. Instead of random theory, interviewers test your ability to apply concepts to practical scenarios, like transforming logs, handling failures, or optimizing queries. That’s why practicing real interview questions gives you a direct edge.

The list below brings together the questions that appear most often in technical interviews for data engineers. Go through them topic by topic, and make your preparation clear, focused, and outcome-driven.

Most Common Data Engineer Technical Interview Questions

SQL & Databases

Write a SQL query to find the second-highest salary in a table.

This question checks how well you handle ranking and filtering — two things data engineers do every day. Interviewers want to see if you can extract specific insights from a dataset without overcomplicating the query. The simplest way is to use a subquery or a window function like DENSE_RANK(). Both show you’re comfortable writing clean, efficient SQL.

What is the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN?

Join types are basic, but this question often reveals if you understand how relational data works in real scenarios. It’s not about memorizing syntax — it's about knowing what data stays and what gets filtered out. A clear, quick answer proves that you're ready to work with multi-table datasets and avoid unwanted data loss.

How do you optimize a slow SQL query?

This one tests problem-solving, not just SQL. The interviewer wants to know if you think like an engineer who can diagnose issues before patching them. Talk briefly about checking execution plans, adding indexes, reducing data scans, or rewriting the query. It shows you’re the type who makes data pipelines faster, cheaper, and cleaner.

What’s the difference between OLTP and OLAP databases?

Here, they're checking if you understand data workflows beyond code. OLTP supports daily app transactions; OLAP fuels analytics and reporting. A strong answer proves you can pick the right storage strategy based on user needs — a key skill for designing reliable data systems.

Explain database normalization. When would you denormalize?

This question isn’t just about theory — it tests your ability to balance performance and data integrity. Normalization reduces redundancy, but denormalization helps with speed. If you explain both sides and mention when you'd make the tradeoff, it shows you're practical and project-focused, not just academic.

What are indexes? How do they affect query performance?

Indexes act like a shortcut to your data. Interviewers ask this to see if you can boost performance without adding new hardware or rewriting systems. A solid response shows you understand not just what indexes are, but when to use them and what impact they have on read vs. write performance.

Python & Data Processing

How do you read a large CSV or JSON file in Python efficiently?

This question tests how you handle data that won't fit in memory — a common situation in real pipelines. It shows whether you can process data in chunks instead of loading everything at once and crashing the system. A strong answer mentions using chunk-based reads in Pandas, iterating over JSON lines, or using Python libraries like ijson for streaming large files.

What’s the difference between a Python list and a NumPy array?

Interviewers ask this to understand if you think about performance — not just writing code, but writing efficient code. Lists are flexible and hold mixed data types, but they don’t support vectorized operations. NumPy arrays are built for numerical tasks and are faster and more memory-friendly. A good answer shows you know when to use each, especially in data-heavy environments.

Explain the difference between Pandas DataFrame and PySpark DataFrame.

This question exposes whether you understand data tools at different scales. Pandas is perfect for small to medium-sized data on a single machine. PySpark is built for distributed computing and can handle massive datasets across clusters. Interviewers want to see if you know when to switch between them instead of forcing one tool to do everything.

How do you handle exceptions or bad data in a pipeline?

This question evaluates your thinking in real-world data scenarios, where data is often messy. They want to know if you can keep a pipeline running even when the data isn’t perfect. Mention validating inputs, using try/except blocks, logging errors, and isolating bad records — it proves you're focused on reliability, not just writing scripts.

Write a function to flatten a nested list in Python.

This shows your ability to solve common algorithm problems without overthinking. Flattening lists tests recursion, iteration, and handling edge cases. Interviewers aren’t grading style — they’re checking if you can write clear, working code that solves the problem without reinventing the wheel.

Data Pipelines & Architecture

What’s the difference between ETL and ELT?

This question checks if you understand how data moves from source to destination. ETL (Extract-Transform-Load) means you clean and shape data before storing it. ELT (Extract-Load-Transform) loads raw data first, then transforms it inside the warehouse. A solid answer shows you can choose between them based on system needs — ETL for strict structure and cleaner storage, ELT for flexibility and modern cloud platforms.

How would you design a pipeline for streaming data (real-time)?

Here, they want to know if you can build a data system that doesn’t wait. Streaming pipelines handle events as they happen — like tracking orders or user clicks. Mention tools like Kafka or Kinesis for ingestion, Spark or Flink for processing, and a data store like PostgreSQL, Elasticsearch, or a data lake for serving. It proves you understand both speed and reliability in real-time systems.

What tools have you used for workflow orchestration (e.g., Airflow)?

This question tests whether you can automate and manage complex workflows instead of running scripts manually. Airflow, Prefect, and Dagster are common answers. Briefly explain how you’ve scheduled jobs, set dependencies, or alerted on failures. Showing experience with retries, parallel tasks, and DAG design supports your ability to run pipelines that don’t break at 3 AM.

How do you handle data schema changes in pipelines?

Pipelines break when data changes — this question checks if you plan ahead. A thoughtful answer includes using schema validation, versioning datasets, or applying tools like Avro or Protobuf with backward compatibility. It shows you're not just building pipelines — you're maintaining them as data evolves.

Big Data & Distributed Computing

Explain how Apache Spark works under the hood.

This question reveals if you understand data processing beyond surface-level usage. Spark uses a cluster of machines to process data in parallel, breaking tasks into smaller units called "jobs" and "tasks" that run across multiple executors. Data is kept in memory when possible to avoid slow disk reads. A strong answer shows you grasp Spark's lazy evaluation, DAG scheduling, and the role of the driver and workers.

What’s the difference between Hadoop and Spark?

Interviewers want to know if you can compare big data tools and pick the right one for the job. Hadoop processes data in batches and writes everything to disk, which is slower but great for long-running jobs. Spark handles both batch and streaming and keeps data in memory, making it much faster. The best answers also note that Spark can run on top of Hadoop’s storage layer (HDFS), so they often work together.

What is partitioning and why is it important?

Partitioning splits data into smaller chunks that can be processed in parallel — this speeds things up and reduces memory pressure on each machine. It also helps organize data by keys like date or region for faster queries. Mentioning partition strategies in Spark, Hive, or data lakes shows you understand how to optimize performance at scale.

How would you store and process petabytes of data?

This question checks if you're comfortable thinking at a massive scale. The interviewer is looking for a combination of cloud object storage (like S3 or GCS), distributed file systems (HDFS), and compute tools like Spark or BigQuery. A complete answer mentions columnar formats like Parquet, partitioning data, and using clusters or serverless tools to run compute efficiently and cost-effectively.

Cloud Platforms & Data Warehousing

How do you design a data warehouse in AWS/GCP/Azure?

This question checks if you understand how to build a structured, scalable environment for analytics. Interviewers want to see how you choose the right storage, compute, and transformation layers. A good answer includes ingesting data into cloud storage (like S3, GCS, or Blob Storage), transforming it with services like AWS Glue or Dataflow, and storing it in a warehouse such as Redshift, BigQuery, or Synapse. It shows you can balance cost, performance, and simplicity while keeping data accessible for BI tools.

What’s the difference between Redshift, BigQuery, and Snowflake?

Here, the goal is to test how well you compare modern data warehouse technologies. Redshift is tightly integrated with AWS and requires cluster management. BigQuery is serverless and scales automatically without worrying about infrastructure. Snowflake runs independently across clouds, separating compute from storage for flexible scaling. Knowing these trade-offs shows you can recommend the right platform based on workload and team needs.

Explain the use of S3 or Blob Storage in data architecture.

This question focuses on how you manage raw and processed data. Object storage like S3 or Azure Blob acts as a central data lake — cheap, scalable, and accessible by multiple services. It’s where raw files land before transformation, backups live for recovery, and analytics tools pull from for queries. A clear answer proves you understand how storage fits into modern data pipelines, not just databases.

What is a data lake and when should it be used?

A data lake stores raw, semi-structured, or unstructured data without a predefined schema. It’s useful when you need flexibility — storing logs, IoT data, or clickstreams before deciding how to use them. Interviewers ask this to see if you understand that not all data is ready for a warehouse. The best answers mention pairing data lakes with warehouses (the “lakehouse” model) to support both exploration and analytics efficiently.

System Design & Scenario Questions

Design a real-time monitoring system for website traffic.

This question checks if you can stitch together an end-to-end streaming path that balances speed, scale, and observability. It shows how you pick ingestion, processing, storage, and alerting without overbuilding. Briefly outline: SDK → Kafka/Kinesis → Spark/Flink for sessionization and aggregations → hot store (Druid/ClickHouse/BigQuery) for sub-second queries → dashboards (Grafana/Looker) with alerts to Slack/PagerDuty, plus late-event handling, deduping, partitions by time, and raw data archived in S3/GCS.

How would you build a data validation framework within a pipeline?

This tests if you protect downstream tables by catching bad records early. It reveals your habits around contracts, monitoring, and safe failure. Describe three gates: ingest (schema/type checks, ranges, uniqueness with Great Expectations/Deequ), transform (referential checks, distribution/drift tests, row-count reconciliation), and publish (final acceptance tests, quarantine or dead-letter on fail). Add logging with batch/file context, alerts on thresholds, retries, and idempotent writes for clean replays.

How do you ensure data security and compliance in pipelines?

This shows whether you handle PII safely and pass audits without slowing delivery. Start with data classification, least-privilege IAM, encryption in transit and at rest (KMS), and secret storage in a vault. Then add column masking/tokenization, row-level filters for multi-tenant data, access logging, key rotation, retention/deletion workflows, and DLP scans—mapped to policies like GDPR/CCPA/HIPAA as needed.

Tips to Prepare for Your Data Engineer Technical Interview

Preparing well makes a big difference in how confidently you answer questions. These tips help you strengthen your core skills and show interviewers you can handle real data engineering work.

—Practice SQL using real datasets so you get faster at joins, window functions, and writing clean queries. Real data forces you to think like an engineer, not a student.

—Build small projects with Python, Spark, or cloud services to show you can turn ideas into working pipelines. Even simple ETL jobs or streaming demos help sharpen practical skills.

—Review fundamentals like data modeling, partitioning, indexing, and distributed computing because these concepts come up in almost every technical round.

—Sketch simple architecture diagrams for practice so you can explain systems clearly during design questions without overthinking.

—Do mock interviews or pair practice to get used to talking through your approach, handling follow-up questions, and staying calm under pressure.

Conclusion

Preparing for a data engineer interview is about understanding how real systems work, not memorizing buzzwords. Each question you face highlights a different part of the job — writing strong SQL, building reliable pipelines, handling large-scale data, or explaining your decisions clearly. When you know why these questions matter, you start answering them with confidence and purpose.

Keep practicing with real data, keep building small but meaningful projects, and keep reviewing the fundamentals that show up in every technical round. The more you work with actual tools and real scenarios, the more natural your answers will feel during interviews.

Above all, stay honest about your experience and focus on how you think through problems. Hiring managers value clarity and practical reasoning more than perfect answers. Show them how you approach challenges, explain your decisions, and back it up with real examples — that’s what sets strong candidates apart. Let your preparation speak for you.