A Databricks data engineer, a highly valued data professional, utilizes the Databricks platform to build, develop, and maintain large-scale data pipelines. They are true experts in data engineering concepts and leverage Databricks’s tools and functionalities to perform their tasks with precision and skill.

Here’s a breakdown of what a Databricks data engineer does:

• Utilize Databricks tools:  They use Apache Spark, Delta Lake, MLflow, Databricks CLI, and REST API to perform their tasks [2]. These tools help with data processing, storage, building machine learning pipelines, and programmatically interacting with the Databricks platform.
• Build Data Pipelines:  They design and implement Extract, Transform, and Load (ETL) pipelines for batch and streaming data using Databricks’s automated ETL processing features [3]. This involves extracting data from various sources, transforming it into a usable format, and loading it into a data warehouse or lake.
• Data Modeling: They design and implement data models using the lakehouse architecture, which combines data warehousing and data lake functionalities [2]. This ensures data is organized for efficient querying and analysis.

As a Databricks data engineer, you play a crucial role in ensuring Data Reliability, Security, and Performance. Your implementation of best practices guarantees that data pipelines are not just reliable and secure, but also perform at their best. This involves data quality checks, data governance practices, and optimizing code for faster processing, all of which significantly impact the overall data ecosystem.