Build and optimize Looker models and dashboards.
Looker LookML Analyst
Looker, LookML, Semantic Layer
Best for
- ▸Building semantic layer views and explores for self-serve analytics teams
- ▸Optimizing LookML model performance with derived tables and PDT strategies
- ▸Implementing row-level security and governance patterns for enterprise Looker deployments
- ▸Migrating existing BI dashboards to Looker with proper dimensional modeling
What you'll get
- ▸Complete LookML view definition with properly typed dimensions, measures, drill fields, and SQL optimizations
- ▸Explore configuration with appropriate joins, filters, and access controls for specific user groups
- ▸PDT implementation with incremental refresh logic and datagroup triggers aligned to ETL schedules
Details about your data warehouse schema, business metrics requirements, user personas, and existing dbt models or raw tables to model against.
Complete LookML code with views, explores, derived tables, plus governance patterns, naming conventions, and performance optimization recommendations.
What's inside
“You are a Looker Analytics Engineer. You design semantic layers, dimensional models, and self-service analytics platforms using LookML. You balance technical expertise with practical business thinking. - Separate business logic (dbt) from presentation logic (LookML): dbt handles transformations and ...”
Covers
Not designed for ↓
- ×Building the underlying data warehouse or ETL pipelines (that's dbt/Airflow territory)
- ×Creating custom visualizations beyond Looker's built-in chart types
- ×Direct SQL database administration or performance tuning at the warehouse level
- ×Setting up Looker infrastructure or managing user provisioning
SupaScore
88.45▼
Evidence Policy
Standard: no explicit evidence policy.
Research Foundation: 7 sources (3 official docs, 1 books, 1 community practice, 1 web, 1 industry frameworks)
This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.
Version History
v5.5 final distill
Pipeline v4: rebuilt with 3 helper skills
Initial release
Prerequisites
Use these skills first for best results.
Works well with
Need more depth?
Specialist skills that go deeper in areas this skill touches.
Common Workflows
Modern Analytics Stack Setup
Transform raw data into governed, performant self-serve analytics: dbt for data modeling, LookML for semantic layer, dashboard design for end-user experience
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