BI ANALYST

Description

Job Summary: A professional responsible for translating business needs into KPIs, building and maintaining dashboards and reports, and ensuring data quality and consistency. Key Highlights: 1. Build and maintain dashboards and reports (Power BI) 2. Perform data processing, integration, and modeling (ETL/ELT) 3. Create and optimize SQL queries (views, procedures) **Responsibilities:** * Gather requirements from business areas and translate them into indicators (KPIs), metrics, and dashboards. * Build and maintain dashboards and reports (Power BI), ensuring usability and visual standardization. * Perform data processing, integration, and modeling (ETL/ELT), focusing on quality, consistency, and traceability. * Create and optimize SQL queries (views, procedures where applicable), ensuring performance and reliability. * Implement data quality validations and controls, identifying anomalies and root causes. * Document business rules (metric definitions), data dictionaries, and update routines. * Support end users (training/adoption) and contribute to the continuous evolution of the analytics environment. **Requirements:** * Advanced SQL (joins, CTEs, window functions, performance tuning, data modeling). * Power BI (DAX, Power Query, data modeling, measures, best practices). * Data modeling (dimensional/star schema; understanding of DW/lakehouse, where applicable). * ETL/ELT concepts, pipelines, incremental updates, and data quality. * Advanced Excel (for support), and desirable: Python for automation/analysis. * Basic governance concepts: catalog, dictionary, access control, versioning. **Responsibilities:** * Gather requirements from business areas and translate them into indicators (KPIs), metrics, and dashboards. * Build and maintain dashboards and reports (Power BI), ensuring usability and visual standardization. * Perform data processing, integration, and modeling (ETL/ELT), focusing on quality, consistency, and traceability. * Create and optimize SQL queries (views, procedures where applicable), ensuring performance and reliability. * Implement data quality validations and controls, identifying anomalies and root causes. * Document business rules (metric definitions), data dictionaries, and update routines. * Support end users (training/adoption) and contribute to the continuous evolution of the analytics environment.