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Omon Ugboke

@omonugboke

Senior Business Intelligence Developer at BRIGHTLINE TRAIN who cut report load times by 30% with optimized Power BI pipelines.

United States

Message

At BRIGHTLINE TRAIN, I developed Power BI reporting solutions and data pipelines for labor market analysis, workforce program tracking, and policy impact assessment. By implementing incremental refresh and optimizing data pipelines, I reduced dashboard load times by 30%.

At DELOITTE, I delivered BI and data engineering work across Power BI, SSRS, Tableau, and enterprise data platforms. Earlier, as a Data Analyst at MEDEXCEL, I built data integration pipelines, reports, and dashboards for healthcare analytics.

Experience

Work history, roles, and key accomplishments

Senior Business Intelligence Developer

Brightline Train

Oct 2022 - Feb 2026 (3 years 4 months)

Architected enterprise-level Power BI, SSRS, and Snowflake-backed reporting solutions, and engineered end-to-end reporting pipelines using Azure Data Factory, SSIS, and Snowflake. Implemented incremental refresh and optimized data pipelines, reducing report load times by 30%.

Power BI SSRS Snowflake Azure Data Factory SSIS Dynamics 365 Dataverse Azure SQL Server DAX Power Query Power Automate Row level security Azure DevOps Confluence

Senior Consultant

Deloitte

Jan 2020 - Sep 2022 (2 years 8 months)

Led full BI development lifecycle including requirements gathering, solution design, development, testing, and deployment using Azure Data Factory, SSIS, and Power BI Service. Developed complex SQL, PL/SQL, Views, CTEs, and Stored Procedures to support scalable reporting and analytics.

Power BI SSRS Tableau Azure Data Factory SSIS Snowflake SQL Pl SQL Databricks Azure Data Lake Azure SQL Azure Synapse Python Power Automate Power BI Desktop DAX Row level security Qlikview

Data Analyst

Medexcel

Jan 2019 - Dec 2019 (11 months)

Designed and developed enterprise SSIS and Azure Data Factory integration pipelines, and built ETL processes to extract, transform, and load operational data into centralized data warehouse and Snowflake environments. Automated ETL scheduling using SQL Server Agent and Azure pipelines, improving data processing efficiency by 30%.