Business Intelligence Analyst

Dimensional Modeling

Por qué: Lets you design slowly changing dimensions so a customer who moves region keeps their historical sales in the old region, and the report stays correct over time.

Data Warehouse Architecture

Por qué: Lets you choose between a Kimball-style mart and a central warehouse for a new business area and place staging, ODS and marts so loads stay maintainable.

SQL Window Functions

Por qué: Lets you build year-over-year comparisons, moving averages and top-N-per-group logic directly in the model, so dashboards stay fast and the logic is reusable.

CTEs & Advanced Aggregation

Por qué: Lets you write recursive CTEs for hierarchies such as org charts or product trees and optimize a slow subquery before it makes a dashboard time out.

ETL Design Patterns

Por qué: Lets you design an incremental, idempotent load so a failed overnight run can be re-run safely without duplicating yesterday's sales.

Data Lake vs Data Warehouse

Por qué: Lets you decide whether raw clickstream belongs in a lake or a warehouse table and explain the cost and query-speed consequences to the team.

Power BI & Tableau Best Practices

Por qué: Lets you build a Power BI or Tableau report with a clean model and DAX or LOD logic, and fix the slow visual that makes executives stop opening the dashboard.

KPI Design & Metrics Strategy

Por qué: Lets you turn a vague request such as grow the business into a metric with a definition, a target and a leading indicator the team can actually move.

BI for Stakeholder Management

Por qué: Lets you run requirement interviews and iterate on a dashboard with users, so what ships answers their real question and gets used after launch.