Terrence Tambinaka

Analyzed large-scale enterprise datasets using SQL, Python (Pandas), Power BI, and Microsoft Excel to identify business trends and deliver actionable insights. Designed and automated interactive Power BI dashboards and KPI scorecards by integrating SQL Server, Snowflake, Azure Data Factory, and REST API data for near real-time visibility. Optimized complex SQL queries and ETL/data transformation pipelines, reducing report execution time by 45% and improving efficiency across 20+ business-critical reporting solutions. Developed predictive and AI-assisted analytics models using Python and Azure AI services, improving forecasting accuracy by 30%. Automated recurring reporting processes with Python, Power Automate, and Power Query, eliminating 25+ hours of manual effort per month and improving reporting accuracy. Implemented enterprise data governance, data quality validation, RBAC, and security best practices aligned with regulatory requirements.

Engineered healthcare reporting solutions by extracting, cleansing, and transforming healthcare data using SQL, Python, and ETL processes to support accurate operational and financial reporting across multiple business units. Built advanced Power BI and Tableau dashboards with dynamic visualizations, drill-through capabilities, and executive KPIs. Improved query and database performance by redesigning SQL logic, indexing strategies, and data models, reducing report generation time by 40% for 150+ business users. Collaborated with data engineers and stakeholders using Agile/Scrum to translate requirements into scalable analytics solutions. Used Azure Data Factory, Snowflake, and cloud platforms to streamline integration workflows for secure, high-volume analytics. Applied statistical and ML concepts to identify utilization trends and forecast operational metrics, improving forecasting accuracy by 20%. Maintained data governance through lineage, metadata management, access controls, and