OBenner/data-engineering-interview-questions
OBenner/data-engineering-interview-questions has 1,810 GitHub stars and 576 forks. GitHot measured +1 stars in 24 hours, +2 in 7 days, and +4 in 15 days. Last checked 2026-11-12 15:18 UTC. Last push 2026-01-13.
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More than 2000+ Data engineer interview questions.
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README.md
OriginalMore than 2000+ questions for preparing a Data Engineer interview.
Full list of questions
Pick a topic below or use the full list to practice end-to-end.
Interview questions for Data Engineer
| Databases and Data Warehouses | ||||
|---|---|---|---|---|
| GitHub Repo | Official page | Questions | Description | Useful links |
| Apache Cassandra | Cassandra is a distributed, wide-column store, NoSQL database management system. | Awesome Cassandra | ||
| Greenplum | Greenplum is a big data technology based on MPP architecture and the Postgres open source database technology. | Awesome Greenplum | ||
| MongoDB | MongoDB is a document-oriented database. | Awesome MongoDB | ||
| Apache Hbase | HBase is an open-source non-relational distributed database. | Awesome HBase | ||
| Apache Hive | Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data query and analysis. | Awesome Hive | ||
| Amazon DynamoDB | Amazon DynamoDB is a fully managed proprietary NoSQL database service. | Awesome DynamoDB Awesome AWS | ||
| Amazon Redshift | Amazon Redshift is a data warehouse product. | Amazon Redshift Utilities Awesome AWS | ||
| BigQuery GCP | BigQuery is a fully-managed, serverless data warehouse. | Awesome BigQuery | ||
| Bigtable GCP | Bigtable is a fully managed wide-column and key-value NoSQL database service. | Awesome Bigtable | ||
| Data Formats | ||||
| Apache Avro | Avro is a row-oriented remote procedure call and data serialization framework. | Awesome Avro | ||
| Apache Parquet | Apache Parquet is a column-oriented data file format designed for efficient data storage and retrieval. | Parquet format · Docs | ||
| Delta | Delta Lake is a storage framework that enables building a Lakehouse architecture with compute engines | Delta examples | ||
| Apache Iceberg | Apache Iceberg is an open table format for huge analytic datasets. | Iceberg docs | ||
| Apache Hudi | Apache Hudi brings upserts, deletes, and incremental processing to data lakes. | Hudi docs | ||
| Big Data Frameworks | ||||
| Apache Airflow | Apache Airflow is a workflow management platform for data engineering pipelines. | Awesome Airflow | ||
| Apache Flume | Apache Flume is a distributed, reliable, and available software for efficiently collecting, aggregating, and moving large amounts of log data. | Flume User Guide | ||
| Apache Hadoop | Apache Hadoop is a collection of software utilities that facilitates using a network of many computers to solve problems involving massive amounts of data and computation. | Awesome Hadoop | ||
| Apache Impala | Apache Impala is a parallel processing SQL query engine for data stored in a computer cluster running Apache Hadoop. | Impala docs | ||
| Apache Kafka | Apache Kafka is a distributed event store and stream-processing platform. | Awesome Kafka | ||
| Apache NiFi | Apache NiFi is a software project designed to automate the flow of data between software systems. | Awesome NiFi | ||
| Apache Spark | Apache Spark is unified analytics engine for large-scale data processing. | Awesome Spark | ||
| Apache Flink | Apache Flink is unified stream-processing and batch-processing framework. | Awesome Flink | ||
| Kubernetes | Kubernetes is a system for managing containerized applications across multiple hosts. | Awesome Kubernetes | ||
| Cloud providers | ||||
| Amazon Web Services | Amazon web service is an online platform that provides scalable and cost-effective cloud computing solutions. | Awesome AWS | ||
| Microsoft Azure | Microsoft Azure is Microsoft's public cloud computing platform. | Awesome Azure | ||
| Google Cloud Platform | Google Cloud Platform is a suite of cloud computing services. | Awesome GCP | ||
| Modern Data Stack | ||||
| dbt | dbt is a transformation framework for building tested and documented SQL models. | dbt tests | ||
| Theory | ||||
| DWH Architectures | A data warehouse architecture is a method of defining the overall architecture of data communication processing and presentation that exist for end-clients computing within the enterprise. | Awesome databases | ||
| Change Data Capture (CDC) | CDC captures inserts/updates/deletes from source systems for low-latency ingestion. | Debezium docs | ||
| Data Modeling | Dimensional modeling concepts used to build reliable analytics datasets. | Kimball Group | ||
| Data Quality | Tests, monitoring, and practices to ensure datasets are trusted and correct. | Great Expectations docs | ||
| Data Observability | Monitoring and incident response practices for pipeline and dataset health. | OpenLineage | ||
| Data Governance | Ownership, policies, privacy, and access controls for data platforms. | DataHub | ||
| Cost Optimization | Practical techniques to reduce compute and storage costs while meeting SLAs. | Spark tuning | ||
| Python for Data Engineering | Python fundamentals for reliable, scalable data pipelines and tooling. | PyArrow docs | ||
| Data System Design | System design interview questions for batch/streaming data platforms. | Data mesh overview | ||
| Data Structures | A data structure is a specialized format for organizing, processing, retrieving and storing data. | Awesome Algorithms | ||
| SQL | SQL is a domain-specific language used in programming and designed for managing data held in a relational database management system (RDBMS). | Awesome SQL | ||
| Data visualization tools/BI | ||||
| Tableau | Tableau is a powerful data visualization tool used in the Business Intelligence. | Tableau Desktop docs | ||
| Looker | Looker is an enterprise platform for BI, data applications, and embedded analytics that helps you explore and share insights in real time. | Looker docs | ||
| Apache Superset | Superset is a modern data exploration and data visualization platform | Superset docs |
Contribution
Please contribute to this repository to help it make better. Any change like new question, code improvement, doc improvement etc is very welcome.
See CONTRIBUTING.md for quick checks and guidelines.
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