Job Description & Details
This is a hands-on analytics engineering role focused on modernizing and scaling data infrastructure using Snowflake and dbt. If you enjoy building clean transformation workflows and are curious about integrating AI coding assistants into your daily data stack, this gig is worth a serious look.
What You'll Actually Be Doing
You'll spend your days designing, building, and optimizing data pipelines that feed core reporting and analytics. This means writing heavy SQL, structuring dbt models, and figuring out why a specific Snowflake warehouse is suddenly spiking compute costs. You will also be actively dogfooding AI tools like Cursor and GitHub Copilot to speed up your boilerplate code, documentation, and debugging processes.
The Core Tech Stack
You need deep fluency in SQL, Snowflake, and dbt to survive here. The data warehouse lives on AWS, so knowing how Snowflake integrates with cloud security and storage is critical. Git is non-negotiable for your daily version control and code reviews. Beyond just knowing the tools, they expect you to think critically about data modeling, automated testing, and keeping pipelines cost-effective.
Interview Expectations
Expect to be grilled on your dbt architecture patterns and how you handle incremental loads vs full refreshes in Snowflake. A hiring manager will likely ask you to optimize a notoriously slow SQL query and explain your cost-reduction strategy. They aren't just looking for syntax; they want to see how you reason about data integrity, handling messy edge cases, and validating the code you write (whether human or AI-generated).
Application Advice
Make sure your resume highlights specific dbt projects where you owned the data modeling layer from scratch. You need to explicitly mention Snowflake performance tuning and AWS integrations to clear the automated ATS filters. Don't just list tools—mention metrics, like how much you reduced pipeline run times or how you implemented data quality tests that saved the analytics team from bad data.