DAY TO DAY:

The engineer develops, maintains, and optimizes data transformation pipelines using DBT and SQL. They build scalable data models that support analytics, reporting, and downstream data needs while ensuring data is accurate, reliable, and efficient. The engineer uses Python and PySpark to implement foundational data engineering changes and complex data transformations. They work within Databricks for compute and storage and perform data integration across multiple data sources and systems. This person develops reusable DBT macros and Jinja templates to improve efficiency, consistency, and maintainability. They apply software engineering best practices, including version control, testing, code reviews, and documentation, while troubleshooting data pipelines and resolving data quality or performance issues. This individual will collaborates with data engineering, analytics, and business teams to understand requirements and deliver reliable, scalable data solutions.

• Building and maintaining data transformation pipelines
• Writing complex SQL and DBT models
• Creating reusable DBT macros using Jinja
• Integrating and transforming data within a lakehouse/data warehouse
• Using PySpark when foundational or lower-level data engineering changes are needed
• Working in Databricks as the compute/storage environment
• Applying software engineering practices to data pipelines