Most Asked Data Engineer Interview Questions with Expert Answers [2025]

Table of Contents

1. SQL and Python Interview Questions
2. ETL and Data Pipeline Questions
3. Big Data Technologies: Hadoop, Spark, Kafka
4. Data Modeling and Warehousing Questions
5. Cloud-Based Data Engineering (AWS, GCP, Azure)
6. Advanced Data Engineering Concepts & Scenario-Based Questions
7. Behavioral and HR Questions for Data Engineers
8. How to Prepare for a Data Engineering Interview
9. FAQs on Data Engineer Interview Questions

Answer: WHERE filters rows before aggregation, while HAVING filters groups after aggregation. For example:

SELECT department, COUNT(*) 
FROM employees 
WHERE status = 'active' 
GROUP BY department 
HAVING COUNT(*) > 10;

Answer: Use GROUP BY with HAVING COUNT(*) > 1:

SELECT email, COUNT(*) 
FROM users 
GROUP BY email 
HAVING COUNT(*) > 1;

Answer: Window functions like RANK() or ROW_NUMBER() operate over a window of rows without collapsing them. Aggregate functions return a single value, while window functions return a value for every row in the window.

Answer: Use IS NULL, IS NOT NULL, or COALESCE(). Be cautious in LEFT JOINs where NULLs may affect filters and conditions.

Answer:

SELECT MAX(salary)
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);

Answer: A decorator is a function that modifies another function’s behavior. They’re useful in logging, monitoring, or retry mechanisms in data pipelines.

def log(func): 
    def wrapper(*args, **kwargs): 
        print(f\"Running  \") 
        return func(*args, **kwargs) 
    return wrapper

Answer: Lists are mutable and ordered. Tuples are immutable and ordered. Sets are unordered and contain unique elements—ideal for removing duplicates in large datasets.

Answer: Use try-except blocks to catch exceptions and optionally log them for debugging. This prevents entire ETL pipelines from failing due to a single bad record.

Answer: Use pandas.read_csv() with chunksize for memory efficiency:

for chunk in pd.read_csv('data.csv', chunksize=10000): 
    process(chunk)

Answer: Lambda functions help in quick transformations—e.g., mapping, filtering, or applying functions inside map(), filter(), or DataFrame.apply().

Answer: In ETL, data is extracted, transformed on a staging server, and then loaded into the data warehouse. In ELT, data is loaded into the warehouse first and then transformed using the warehouse’s computing power. ELT is preferred in cloud-native stacks like Snowflake or BigQuery due to their scalability.

Answer: Key principles include:

• Use idempotent operations to avoid duplicates
• Implement logging and alerting for observability
• Separate config, logic, and data access layers
• Leverage orchestration tools like Airflow or Prefect to manage dependencies

Answer: Some challenges include:

• Data quality issues (nulls, schema drift)
• Late-arriving or out-of-order data
• Scaling batch jobs under high volume
• Orchestrating dependencies across sources

Answer: Idempotency means that running the same ETL task multiple times does not change the result beyond the first execution. It ensures that retries or re-runs don’t create duplicates or corrupt outputs—critical for reliability in production pipelines.

Answer: Use schema validation tools (like Great Expectations) and incorporate versioning. You can also create fallback logic to handle new/unknown fields and set alerts for breaking changes. In dbt, tests like dbt test --store-failures help flag issues early.

Answer: Implement unit and integration tests using frameworks like pytest and dbt tests. Add logging at key transformation steps, use data validation (row counts, null checks), and set up Airflow sensors or Prometheus for runtime monitoring.

Answer: Airflow is a workflow orchestration tool used to author, schedule, and monitor complex ETL jobs. It helps define data dependencies using DAGs (Directed Acyclic Graphs) and provides retry, alerting, and execution history out of the box.

Answer: Batch pipelines process data at fixed intervals (e.g., daily reports), while streaming pipelines ingest and process data continuously (e.g., fraud detection). Streaming is typically built using Kafka, Spark Streaming, or Flink, whereas batch may use Airflow, dbt, or Glue.

Answer: Use transactions or atomic operations where possible, validate intermediate outputs, enable audit trails (e.g., row hashes, checkpoints), and implement pipeline lineage tracking using tools like OpenLineage or Marquez.

Answer: Interviewers expect specifics here. Mention tools like:

• Airflow: DAGs, task dependencies, custom operators
• dbt: modular SQL modeling, testing, documentation
• Fivetran/Stitch: plug-and-play connectors for SaaS data
• Kafka: stream ingestion and integration into pipelines

Answer: Key components of Hadoop include:

• HDFS (Hadoop Distributed File System): A scalable storage layer for managing large datasets across clusters.
• MapReduce: A programming model for processing big data in parallel.
• YARN: A resource manager that handles cluster resource allocation and job scheduling.
• Other tools include Hive (SQL querying), Pig (data flow scripting), and HBase (NoSQL database).

Answer: Hadoop is suitable for long-running, batch-oriented jobs and when cost-effective storage is critical. Spark is more efficient for iterative and real-time workloads due to its in-memory processing. Spark has largely replaced MapReduce for most modern workloads due to its speed and developer flexibility.

Answer: RDD (Resilient Distributed Dataset) is the fundamental data structure in Spark, representing an immutable, distributed collection of objects. DataFrames provide higher-level abstraction, are optimized via Catalyst and Tungsten engines, and are preferred for SQL-style queries and transformations due to their performance benefits.

Answer: In Spark, transformations like map(), filter(), or groupBy() are lazily evaluated. This means they’re not executed immediately; instead, Spark builds a logical execution plan (DAG) and only processes the data when an action (like collect() or write()) is called. This allows Spark to optimize execution and reduce data shuffling.

Answer: Partitions determine how Spark splits data across worker nodes for parallel processing. Too few partitions can underutilize cluster resources; too many can cause overhead. Proper partitioning improves performance and minimizes shuffle operations during joins and aggregations.

Answer: Kafka is a distributed streaming platform used for building real-time data pipelines and streaming applications. It acts as a high-throughput, fault-tolerant message broker that decouples producers (data sources) and consumers (data sinks). Data engineers use Kafka to stream logs, sensor data, or event-driven transactions across systems.

Answer: A Kafka topic is a named stream where messages are published. Each topic is split into partitions for parallelism and scalability. Partitions ensure that multiple consumers can read data in parallel, enabling high-throughput stream processing.

Answer: Kafka achieves fault tolerance through replication. Each partition can have multiple replicas across different brokers. Data is persisted to disk and can be retained for a configurable period, ensuring durability even if consumers fail to consume it immediately.

Answer: Kafka is designed for high-volume, distributed, and real-time data ingestion. Unlike RabbitMQ, Kafka stores messages on disk and supports message replay. It also scales better with partitions and consumer groups. Kafka is ideal for event-driven architectures and analytics use cases.

Answer: Tailor this answer to your experience. For example:

“At my previous role, I designed a real-time fraud detection pipeline using Kafka for event ingestion, Spark Streaming for processing, and Elasticsearch for storing anomalies. We scaled to 500K messages/minute and implemented alerting using Grafana and Prometheus.”

Answer: OLTP (Online Transaction Processing) systems handle real-time operations with frequent reads and writes (e.g., banking systems). OLAP (Online Analytical Processing) systems are designed for complex queries and analytics on historical data. Data warehouses are optimized for OLAP workloads.

Answer: A star schema consists of a central fact table linked to multiple dimension tables. It is simple, query-efficient, and widely used in reporting systems. It allows users to slice and dice data across various dimensions like time, geography, and product.

Answer: In a snowflake schema, dimension tables are normalized into multiple related tables. This reduces data redundancy but adds complexity to queries. It’s typically used when storage efficiency or multi-level hierarchies are critical.

Answer: SCDs are dimensions where attribute values can change over time. There are several types:

• Type 1: Overwrite the old value
• Type 2: Add a new row with versioning
• Type 3: Add a new column for the historical value

Answer: Choose partitions based on access patterns—commonly by date, region, or customer ID. The goal is to reduce the amount of scanned data during queries. Avoid high cardinality columns and monitor skew in partition sizes.

Answer: Normalization reduces redundancy and improves data integrity, typically used in OLTP. Denormalization improves read performance by reducing joins—used in OLAP systems. Most analytical warehouses use a denormalized (flattened) schema for speed.

Answer: Implement referential integrity checks, use surrogate keys, and apply ETL constraints to validate dimensional lookups. Tools like dbt can also enforce data tests (e.g., non-null joins, unique keys) to catch mismatches early.

Answer: Schema evolution refers to the ability to adapt to changing table structures (e.g., new columns). In cloud warehouses like BigQuery or Snowflake, use schema auto-detection or version-controlled dbt models. Always validate backward compatibility and downstream impact before deploying changes.

Answer: Columnar storage enables high-performance analytical queries by reading only the necessary columns instead of entire rows. It also supports better compression, leading to storage savings and faster scans in tools like Redshift, BigQuery, and Snowflake.

Answer: Materialized views store precomputed query results, making reporting faster. Use them for repetitive, expensive aggregations (e.g., daily sales rollups). They can be scheduled for refresh or triggered automatically in modern warehouses.

Answer: Cloud-based pipelines offer scalability, lower infrastructure overhead, pay-as-you-go pricing, and faster deployment cycles. Services like AWS Glue or GCP Dataflow allow engineers to focus on logic rather than server management. They also integrate easily with cloud-native storage, compute, and monitoring tools.

Answer:

• Redshift: Cluster-based, more control over performance tuning, supports complex joins and nested data.
• BigQuery: Serverless, scales automatically, ideal for ad-hoc SQL analytics, with built-in ML and GIS support.
• Redshift suits predictable, high-volume workloads; BigQuery is great for variable or exploratory analysis.

Answer: AWS Glue is a serverless ETL service that automates job scheduling, dependency tracking, and code generation. It supports Spark under the hood and integrates with Redshift, S3, and RDS. Glue Data Catalog also provides metadata management across services.

Answer:

• Use event-driven architecture (e.g., Cloud Functions, Lambda triggers)
• Decouple compute from storage (S3, GCS, ADLS)
• Build idempotent, retry-safe ETL jobs
• Use managed orchestration tools like Cloud Composer or Azure Data Factory

Answer: Implement IAM roles and fine-grained access policies. Use encryption at rest and in transit (e.g., KMS, TLS). Monitor access logs via services like AWS CloudTrail or GCP Audit Logs. Apply data classification tags and restrict PII access.

Answer: Terraform allows infrastructure-as-code provisioning. You can version control and automate the deployment of cloud services like S3 buckets, BigQuery datasets, or IAM roles. It ensures reproducibility and reduces manual config drift across environments.

Answer: Use platform-native tools: AWS CloudWatch, GCP Operations Suite (Stackdriver), and Azure Monitor. Collect logs, set up metrics dashboards, and implement alerting. For job-level monitoring, use Airflow UI, Glue job logs, or Dataflow logs.

Answer: Serverless pipelines (e.g., AWS Lambda, GCP Cloud Functions) scale automatically and abstract infrastructure. They’re ideal for event-triggered workflows. Container-based (e.g., AWS Fargate, GKE, AKS) offers more control and is better for complex workloads needing custom libraries or long runtimes.

Answer: Metadata describes structure, lineage, and context. Tools like AWS Glue Data Catalog, GCP Data Catalog, or Azure Purview centralize metadata and enable governance, discovery, and access control. This is critical for scaling pipelines across teams and environments.

Answer: Tailor this to your experience. Example:

“I built a serverless ETL workflow using AWS Lambda to process daily logs from S3, transform them with Glue, and load the results into Redshift. We used CloudWatch for monitoring, and IAM policies to restrict access to only necessary resources.”

Answer: Use watermarking and windowing in streaming frameworks like Apache Flink or Spark Structured Streaming. For batch pipelines, implement a staging layer and periodic backfills. Always store event-time metadata and design partitioning strategies that allow appends or corrections without overwriting valid data.

Answer: Use schema registries (e.g., Confluent Schema Registry) for version control and compatibility checks in streaming. In batch systems, validate schemas at ingestion and use tools like dbt for versioned model management. Avoid SELECT * queries to prevent breakage due to added columns.

Answer: CAP stands for Consistency, Availability, and Partition Tolerance. A distributed system can only guarantee two of these at any given time. For example, Cassandra sacrifices consistency to maximize availability and partition tolerance, while relational databases often prioritize consistency and availability.

Answer: Data lineage tracks the journey of data—where it originated, how it transformed, and where it ended up. It’s critical for debugging, compliance (e.g., GDPR), auditing, and improving trust in downstream systems. Tools like DataHub or Amundsen help visualize lineage across pipelines.

Answer: Use Git for version control, integrate with CI tools like GitHub Actions or Jenkins, and set up automated tests for SQL logic (e.g., dbt tests), linting, and deployment. Infrastructure can be provisioned using Terraform or Helm. Use a staging environment to test all changes before going live.

Answer: Partition by low-cardinality, high-filter-usage fields like date or region. Avoid over-partitioning (e.g., by user ID). Use formats like Delta Lake or Apache Iceberg which support dynamic partitioning and optimize file sizes. Monitor skew and storage growth continuously.

Answer: Introduce checkpoints in your pipeline with validation rules—null checks, data type constraints, uniqueness tests. Use tools like Great Expectations or Monte Carlo to automate profiling and monitor for schema drift or anomaly detection across time windows.

Answer: Use dbt when working in SQL-first environments with cloud warehouses like BigQuery or Snowflake. Choose Spark for large-scale distributed processing where you need programmatic control or support for multiple formats (e.g., Parquet, Avro). dbt is preferred for analytics workflows; Spark is better for compute-heavy batch jobs.

Answer: Version control your pipeline logic and configs using Git. Use pinned dependencies and containerized environments (Docker). Store dataset snapshots or use time-travel-enabled formats (e.g., Delta Lake, BigQuery). Document assumptions and output contracts for each pipeline stage.

Answer: Techniques include query optimization, reducing scan volume via partition pruning, using materialized views, autoscaling compute resources, and monitoring usage with budget alerts. For compute-heavy jobs, use preemptible or spot instances. Always separate dev/test/prod environments to avoid uncontrolled cost spikes.

Answer: Frame your response using the STAR method. Explain the incident, how you diagnosed the root cause, involved stakeholders, restored service, and implemented preventive monitoring or alerts. Highlight your ownership and communication clarity.

Answer: Mention frameworks like Eisenhower Matrix or Agile sprints. Explain how you balance high-priority business needs with technical debt and proactively flag risk if bandwidth becomes a blocker.

Answer: Share how you translated pipeline logic, schema decisions, or latency issues into business-friendly language. Demonstrate your ability to bridge tech and business goals—a key skill in modern data teams.