A data engineer's role is conceptually similar to that of a data analyst, but with a focus on handling semi-structured, unstructured, and streaming data. They rely on tools like Airflow, dbt, Fivetran, or Airbyte to extract, transform, and load (ETL) data, often using an ELT process. Data engineers are responsible for creating repeatable engineering processes to support other parts of the organization, whereas data scientists focus on tackling data science or machine learning problems, using software like Scikit-learn, TensorFlow, or PyTorch. The main difference between a data analyst and a data engineer is that the former typically deals with one-off report generation for business intelligence, while the latter specializes in handling complex data processing tasks. Data engineers are also more likely to work on projects involving event-driven architecture, real-time streaming analytics, and software engineering, whereas data analysts focus on business intelligence problems.