Data engineers are responsible for designing, building, and maintaining the infrastructure and tools needed to collect, store, process, and analyze data. Some of the main tools used by data engineers include:

1. Data storage and processing technologies: Data engineers use technologies such as Hadoop, Spark, and Kafka to store and process large amounts of data. They also use NoSQL databases such as MongoDB, Cassandra, and HBase to store unstructured data.
2. Data integration and ETL tools: Data engineers use tools such as Apache Nifi, Talend, and Informatica to extract, transform, and load data from various sources into a central data store.
3. Data warehousing technologies: Data engineers use technologies such as Amazon Redshift, Google BigQuery, and Snowflake to design, build and maintain data warehouses.
4. Cloud platforms: Data engineers use cloud platforms such as AWS, Azure, and GCP to build, deploy, and manage their data pipelines and infrastructure.
5. Data governance and security tools: Data engineers use tools such as Apache Atlas and Apache Ranger to manage data governance and security.
6. Monitoring and management tools: Data engineers use tools such as Apache Ambari, Cloudera Manager, and Datadog to monitor and manage their data infrastructure.
7. Version control and collaboration tools: Data engineers use tools such as Git, GitHub and GitLab to collaborate with other team members, share code and track the progress of their projects.
8. Containerization and orchestration tools: Data engineers use tools such as Docker and Kubernetes to package and deploy their data pipelines and infrastructure in a consistent and repeatable way.

These are some of the main tools used by data engineers, but the specific tools and technologies used will vary depending on the project and the data engineer’s personal preferences. It is also worth noting that the skills and responsibilities of a data engineer can overlap with those of a data scientist, and data engineers often use similar tools as data scientists, such as Python, R, and SQL.