daemon-blockint-tech Business Intelligence Analyst
Overview
Design dashboards, write analytical SQL, define KPIs, and manage stakeholder analytics requirements. This skill covers the full BI analyst workflow from dashboard design and chart selection through analytical SQL patterns, metric definition templates, and stakeholder engagement processes.
Features
- Chart selection guidance for different analytical questions
- SQL pattern library for cohort, funnel, retention, and cumulative analysis
- Metric definition templates with formula, numerator, denominator, and data source
- Stakeholder interview and engagement workflow
- BI tool patterns for Tableau, Looker, and Power BI
Usage
- Identify the user's BI need (dashboard, SQL analysis, metrics, or stakeholder work)
- Follow the corresponding workflow below
- Produce structured outputs: dashboard wireframes, SQL queries, metric definitions, or stakeholder interview notes
Examples
User: "Build a retention dashboard" Agent: Runs Dashboard Design workflow, selects line chart for retention curves, applies F-pattern hierarchy, adds benchmark context
User: "Write SQL for cohort analysis" Agent: Runs Analytical SQL workflow, uses self-join on first-event date pattern, returns cohort retention table
User: "Define our churn metric" Agent: Runs Reporting & Metrics workflow, fills metric definition template with formula, numerator, denominator, data source
When to Use
- Building or revising dashboards and self-serve BI reports
- Writing analytical SQL for metrics, cohorts, funnels, or retention
- Defining, documenting, or reconciling KPIs and business metrics
- Presenting data insights or eliciting analytics requirements from stakeholders
When NOT to Use
- Enterprise data platform, mesh, or governance architecture → use
data-architect - Warehouse ETL design, incremental loads, or platform-specific tuning → use
data-warehouse-engineer - dbt marts, incremental models, data tests, and docs/lineage → use
analytics-data-engineer - Predictive modeling, experiment design, or ML productionization → use
data-scientist - Business process mapping or BRD/FRD requirements without analytics delivery → use
business-analyst - Business model research, market sizing, unit economics modeling → use
business-model-researcher
Core Workflows
1. Dashboard Design
Design checklist:
Define the audience and action
- Who uses this dashboard? How often?
- What decision does it support?
- What action should they take after viewing?
Choose the right charts
Question Chart Type How much/many? KPI cards, bar charts How does it change over time? Line charts, area charts How is it distributed? Histograms, box plots How do parts relate to the whole? Pie charts (limited), treemaps, stacked bars How do variables relate? Scatter plots, heatmaps Where is it happening? Maps, geo charts Apply visual hierarchy
- Most important metrics at top left (F-pattern reading)
- Use size and color for emphasis, not decoration
- Limit to 3-5 colors per dashboard
- Consistent formatting across all dashboards
Add context
- Benchmarks, targets, or prior period comparisons
- Annotations for significant events
- Last refresh timestamp
2. Analytical SQL
Common analysis patterns:
| Analysis | SQL Pattern |
|---|---|
| Month-over-month growth | LAG() window function |
| Running total | SUM() OVER (ORDER BY date) |
| Top N per group | ROW_NUMBER() OVER (PARTITION BY group ORDER BY metric DESC) |
| Cohort retention | Self-join on first-event date |
| Funnel conversion | COUNT(DISTINCT CASE WHEN step = N THEN user_id END) |
| Cumulative distinct | COUNT(DISTINCT user_id) OVER (ORDER BY date) |
3. Reporting & Metrics
Metric definition template:
## [Metric Name]
**Definition:** [Clear, unambiguous description]
**Formula:** [Mathematical formula or SQL pseudocode]
**Numerator:** [What is counted]
**Denominator:** [The population, if a rate/ratio]
**Data source:** [Table(s) used]
**Dimensions:** [How it can be sliced: date, region, product]
**Owner:** [Who maintains this definition]
**Last updated:** [Date]
4. Stakeholder Management
Engagement workflow:
- Discovery: Interview stakeholders to understand business questions
- Prototype: Build a quick draft with sample data
- Review: Walk through with stakeholders; capture feedback
- Refine: Iterate based on feedback (limit to 2-3 rounds)
- Deliver: Deploy with documentation and training
- Maintain: Schedule quarterly reviews for relevance