Snowflake engineer
Senior Snowflake / DBT Data Modeling Expert
Location: Spring, TX (Onsite)
Interview will be in-person Technical Requirements Snowflake and dbt Extensive, hands-on experience with Snowflake and dbt is essential. Candidates must have practical experience managing data transformations, testing, data quality, and preparing data for consumption. Must understand the complete data engineering process, not just one component. Candidates must demonstrate experience from actual implementation rather than knowledge gained through team exposure alone. Data Modeling and Star SchemaStrong Star Schema modeling expertise is one of the most important requirements. Candidates must understand good versus poor modeling practices and be able to explain their design decisions. The manager expects candidates to work through a straightforward modeling problem in approximately five minutes during the initial interview assessment.
Supporting Technologies:
Azure Data Factory (ADF) for data ingestion.
GitHub Actions for development workflows and governance.
Terraform for infrastructure as code.
REST APIs and appropriate data access practices.
SQL optimization, automation, and performance improvement.
AI experience is a plus, particularly experience using AI to improve development workflows.
End-to-End Data Engineering:
Data enters through ADF and moves into the data lake.
The environment uses bronze, gold, and platinum data layers.dbt manages transformations, testing, cleanup, and preparation for consumption.
Data is exposed through APIs and used in Power BI semantic models and dashboards.
The role focuses on data engineering, architecture, and modeling—not Power BI dashboard development.