Olugbenga Ajiga

Built Sense's first cloud data platform from a blank slate.
Designed and shipped a Microsoft Fabric Lakehouse from the ground up using Medallion architecture (Bronze-Silver-Gold).
Implemented OneLake storage, RBAC governance, secure ADF-orchestrated ingestion pipelines, and Cyber Essentials-compliant data products.
Authored the organization's first enterprise data governance framework.
Produced platform architecture blueprints, data standards, naming conventions, pipeline documentation, and quality policies adopted across IT, engineering, and all business teams.
Orchestrated production-grade ETL/ELT pipelines via SSIS and Azure Data Factory.
Ingested 2,500+ daily records from HR (iTrent), operational systems, and third-party sources into a central SQL Server data warehouse.
Embedded data quality checks at every ingestion layer.
Launched a self-serve Power BI analytics suite end-to-end.
Built full data modeling, advanced DAX measures, calculated columns, row-level security, and scheduled refresh.
Cut user provisioning time by 95% by re-engineering the HR-to-Active Directory integration pipeline end-to-end.
Acted as trusted data advisor to IT leadership and the board.
Aligned the data architecture roadmap with the wider technology strategy.
Translated engineering decisions into language senior leadership could evaluate and act on.

Built Sense's first cloud data platform from a blank slate, Authored the organization's first enterprise data governance framework, Orchestrated production-grade ETL/ELT pipelines via SSIS and Azure Data Factory, Launched a self-serve Power BI analytics suite end-to-end, Cut user provisioning time by 95%, Acted as trusted data advisor to IT leadership and the board

Led full migration of legacy data infrastructure to Azure.
Transitioned the FUTURA CRM and full reporting stack from on-premise SQL Server to Azure SQL and Databricks.
Migrated 70 SSIS packages, 80+ reports, and 100+ stored procedures with zero loss of business continuity.
Improved SQL Server warehouse performance by 58%.
Reduced daily load times from 6 hours to 2.5 hours through advanced T-SQL indexing, query restructuring, and physical data model tuning.
Raised data accuracy to 95% by implementing systematic T-SQL data quality monitoring across MySQL and SQL Server environments.
Cut support tickets by 20%.
Slashed manual reconciliation time by 80% from 4 hours to 45 minutes per day by re-engineering SSIS ETL workflows with real-time inventory integration.
Designed and deployed compliant ELT pipelines for 53,500+ daily energy transactions.
Built policy-enforced, governance-controlled data flows for smart meter and commercial datasets with 100% compliance with Ofgem mandates.
Consolidated multi-source executive BI dashboards.
Integrated SQL Server, Excel, and REST APIs into a unified Power BI and SSRS reporting layer.
Gave commercial leadership a single, trusted view of KPIs, margins, and energy performance.

Led full migration of legacy data infrastructure to Azure, Improved SQL Server warehouse performance by 58%, Raised data accuracy to 95%, Slashed manual reconciliation time by 80%, Designed and deployed compliant ELT pipelines for daily energy transactions

Designed and deployed GCP analytics pipelines (BigQuery + Cloud Storage) for a major NGO client.
Ingested, transformed, and modeled donor and campaign data into Power BI dashboards.
Reduced monthly reporting time by 50%.
Enabled real-time performance tracking.
Presented monthly analytical findings to C-suite stakeholders.
Translated complex datasets into clear narratives that shaped strategic planning, donor strategy, and resource allocation decisions.
Restored data integrity across 17,600+ CRM records.
Wrote T-SQL transformation scripts to resolve structural discrepancies between Dynamics 365 and the data warehouse.
Produced clean analytics-ready datasets.
Improved downstream reporting accuracy.
Systematized finance reconciliation pipelines through SQL scripting and Power BI workflow redesign.
Trimmed monthly processing time by 30%.
Saved 12+ hours of manual effort every month.
Shipped the product MVP 3 weeks ahead of schedule for an asset management scheduling tool.
Used disciplined Agile delivery, data dependency mapping, and clear sprint prioritization within a Scrum environment.

Designed and deployed GCP analytics pipelines for a major NGO client, Presented monthly analytical findings to C-suite stakeholders, Restored data integrity across CRM records, Systematized finance reconciliation pipelines, Shipped the product MVP 3 weeks ahead of schedule

Architected and built an enterprise SQL Server data warehouse handling 4,000,000+ monthly queries across logistics, supply chain, HR, and operations.
Led end-to-end engineering from requirements to production.
Generated $500,000 in measured business value through the Microsoft BI Stack (SSAS, SSRS, Power BI).
Generated $500,000 in vendor savings by governing 15+ third-party data integrations.
Evaluated, standardized, and onboarded each integration with consistent SSIS architecture patterns.
Achieved a 95% on-time delivery rate.
Engineered a Python-based biometric attendance data service.
Processed 10,000+ employee records automatically.
Eliminated 80% of manual data entry.
Improved payroll data accuracy organization-wide.
Migrated the Weighbridge Management System from MS Access to MSSQL Server.
Achieved a 40% system efficiency improvement.
Saved $50,000 in licensing costs.
Reduced data retrieval times by 40% through advanced SQL Server architecture, physical data model optimization, and rigorous query engineering.
Deployed hybrid cloud infrastructure across Azure, AWS (S3, EC2), and GCP.
Designed the multi-cloud blueprint to support enterprise BI, advanced analytics, and cross-cloud data integration at manufacturing scale.

Architected and built an enterprise SQL Server data warehouse, Generated $500,000 in vendor savings, Engineered a Python-based biometric attendance data service, Migrated the Weighbridge Management System, Reduced data retrieval times by 40%, Deployed hybrid cloud infrastructure across Azure, AWS, and GCP