Job Description & Details
This is a core enterprise analytics role focused entirely on scaling Looker and building out a bulletproof semantic layer for cross-functional teams. If you are tired of ad-hoc dashboard chaos and want to build clean, maintainable LookML models that act as the single source of truth, this position gives you the autonomy to do it right.
What You'll Actually Be Doing
Your days will be split between writing advanced LookML, optimizing heavy queries, and wrangling stakeholders across product, finance, and marketing who all want custom metrics. You'll spend a significant amount of time building persistent derived tables (PDTs), implementing liquid parameters, and setting up role-based security. Expect to push back on redundant dashboard requests and steer the organization toward centralized, self-service analytics.
The Core Tech Stack
You need absolute fluency in LookML and complex SQL to survive here. The stack leans heavily on Google Cloud Looker integrated with modern cloud data warehouses like BigQuery, Snowflake, or Redshift. Experience with Git is non-negotiable for managing code deployments, and familiarity with dbt for upstream modeling will give you a massive edge since you'll be partnering closely with data engineers.
Interview Expectations
Expect to be tested hard on your LookML design patterns, particularly how you handle symmetric aggregates, fan-out traps, and performance tuning for sluggish explores. The hiring manager is going to want to hear about a time you refactored a massive, broken data model, so come prepared with a specific war story on how you optimized a query or structured a complex PDT without bringing the warehouse to a crawl.
Application Advice
Make sure your resume explicitly highlights your hands-on experience with advanced LookML features like liquid parameters and user attributes, rather than just listing basic dashboard creation. If you have the Looker Certified LookML Developer badge, put it right at the top. Tailor your bullet points to showcase how your semantic layers directly reduced data discrepancies and improved query performance for business leaders.