BI ANALYST

Description

Job Summary: A professional responsible for gathering requirements, building and maintaining dashboards, and performing data modeling and quality assurance to enhance the analytical environment. Key Highlights: 1. Dashboard and report development and maintenance (Power BI) 2. Data modeling and data processing (ETL/ELT) 3. SQL query optimization and data quality **Responsibilities:** * Gather requirements from business units and translate needs 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), with a focus 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 analytical environment. **Requirements:** * Advanced SQL (joins, CTEs, window functions, performance tuning, modeling). * Power BI (DAX, Power Query, 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 (supporting tool), and preferably: Python for automation/analysis. * Basic governance knowledge: catalog, data dictionary, access control, versioning. **Responsibilities:** * Gather requirements from business units and translate needs 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), with a focus 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 analytical environment.