Job Description & Details
This is a hands-on analytics engineering role focused on modern cloud data stacks where you will actually spend time optimizing pipelines rather than just talking about them. If you enjoy wrangling complex data models and want to experiment with AI-assisted workflows, this gig in Sunnyvale offers a solid platform to build scalable data infrastructure. You'll be bridging the gap between raw data storage and business-ready analytics while keeping performance and cost in check.
What You'll Actually Be Doing
You will spend your days designing, building, and refining data pipelines and transformation workflows using dbt and Snowflake. Expect to troubleshoot flaky models, optimize expensive SQL queries, and set up robust data quality tests so downstream stakeholders don't come knocking with broken dashboards. A unique part of this role is actively incorporating AI coding tools like GitHub Copilot or Cursor into your daily development cycle to speed up boilerplate creation and debugging.
The Core Tech Stack
You'll need deep, production-level proficiency in SQL, Snowflake, and dbt. Since the data warehouse lives on AWS, understanding cloud integrations, security policies, and connectivity is non-negotiable. Version control via Git and a solid grasp of CI/CD practices will anchor your daily code reviews and deployments.
Interview Expectations
Expect to be grilled on how you structure complex dbt incremental models and handle late-arriving data. The hiring manager will likely ask you to write a complex SQL window function on the spot and explain how you would optimize a Snowflake query that's currently timing out. They aren't just looking for syntax; they want to see your troubleshooting methodology and cost-awareness in a cloud warehouse.
Application Advice
Make sure your resume highlights specific metrics around pipeline performance improvements, cost reductions in Snowflake, and complex data models you've built using dbt. Don't just list keywords like AWS and Git; explicitly mention your experience with modern data governance, automated testing, and how you validate AI-generated code to ensure production stability.