BI Dashboards

Creates simple dashboards in Metabase/Looker/Tableau based on prepared dbt models. Understands data visualization principles: chart type selection, filters, drill-down. Works with the mart layer as the primary source for BI.

Builds basic dashboards in Tableau and Power BI following team templates. Understands core KPI definitions and applies standard visualization types for executive reporting. Follows BI style guides and documentation for consistent output.

Creates exploratory dashboards with basic filters and drill-downs. Understands standard chart types for statistical visualization. Builds simple cohort views and follows team conventions for dashboard layout and naming.

Designs analytical dashboards with correct business logic: metric calculation at the BI vs dbt level, parameterized reports, cross-filtering. Optimizes dashboard performance through proper data modeling in the mart layer.

Independently designs interactive dashboards in Tableau and Power BI with calculated fields and LOD expressions. Optimizes query performance for large datasets. Implements self-service BI layers enabling business users to explore KPIs autonomously.

Independently builds analytical dashboards with advanced statistical visualizations and dynamic cohort analysis. Optimizes dashboard performance through query tuning and data extracts. Creates A/B test dashboards with significance indicators and confidence intervals.

Defines BI development standards: semantic layer / LookML / Tableau data models for metric consistency, dashboard templates for typical business tasks. Optimizes dbt model and BI interaction through extract-based or live connection approaches.

Designs enterprise BI architecture across Tableau, Power BI, and Looker with governed data models and semantic layers. Optimizes dashboard ecosystems for thousands of concurrent users. Implements data quality frameworks and row-level security for executive reporting.

Designs scalable dashboard architecture for advanced statistical visualization and cross-functional cohort analysis. Optimizes for big data sources with incremental refresh and materialized views. Implements data governance ensuring metric consistency across A/B test and analytics dashboards.

Defines the organization's BI strategy: tool selection and standardization (Looker vs Tableau vs Metabase), governance for metrics and dashboards, self-service analytics for business users. Implements a semantic layer for unified metric definitions.

Defines enterprise BI strategy and dashboard platform roadmap. Shapes self-service BI culture enabling business-driven analytics. Coordinates BI teams across departments and standardizes KPI definitions. Optimizes hybrid approaches combining Tableau, Power BI, and Looker ecosystems.

Defines analytics dashboard strategy and visualization standards across the organization. Shapes the analytical platform enabling self-service cohort analysis and A/B test reporting. Coordinates analytics teams and establishes metric governance ensuring statistical rigor in all dashboards.

Architects enterprise BI: multi-tool strategy for different audiences, embedded analytics for products, real-time dashboards. Defines the roadmap from traditional BI to self-service analytics and metrics layer.

Defines organizational BI strategy aligning dashboard platforms with business objectives. Designs enterprise-wide self-service BI architecture spanning Tableau, Power BI, and Looker. Establishes cross-departmental KPI governance framework ensuring data-driven decision-making at executive level.

Defines organizational analytics visualization strategy connecting dashboard platforms to data mesh architecture. Designs enterprise analytical framework for statistical reporting, cohort analysis, and experimentation. Establishes governance standards ensuring analytical rigor and metric consistency organization-wide.