Job Description & Details
This is a heavy-hitting BI engineering role where you'll own the semantic layer and enterprise dashboard architecture using modern cloud tools. If you enjoy translating messy business requirements into clean, performant Looker and Tableau models built on top of BigQuery, this gig offers a solid platform to showcase your data architecture chops.
What You'll Actually Be Doing
You'll spend your days buried in BigQuery optimizing complex SQL queries and collaborating directly with stakeholders to figure out why their metrics don't match. Expect to build resilient semantic layers, design self-service dashboards that non-technical leaders won't break, and occasionally field urgent requests for executive KPI scorecards right before a board meeting. It's a mix of data modeling, performance tuning, and evangelizing clean data governance across the organization.
The Core Tech Stack
Looker, Tableau, and Google BigQuery on Google Cloud Platform are non-negotiable here. You need to know how to write window functions and optimize massive SQL datasets without blowing up cloud compute costs. Having dbt or Airflow in your toolkit will also save you countless headaches when you need to wrangle downstream pipelines.
Interview Expectations
Expect to be grilled on how you architect semantic layers in Looker versus Tableau, specifically regarding how you handle caching, derived tables, and complex joins at scale. They will likely ask you to whiteboard a scenario where a critical dashboard is timing out on BigQuery and walk through your exact step-by-step query optimization process.
Application Advice
Make sure your resume doesn't just list tool names, but explicitly highlights your experience with semantic modeling and performance tuning in BigQuery. If you've built self-service frameworks or integrated predictive analytics using Python or Vertex AI, bring those front and center to clear the ATS and catch the hiring manager's eye.