Google Certified Professional Data Engineer with a foundational background in financial data architecture and 5+ years of combined experience designing high-integrity automated data systems, ETL/ELT pipelines, and scalable cloud infrastructure.
Expert at mapping complex business logic into clean SQL, Python-driven automation frameworks, and managed containerized workflows (Docker) that eliminate operational blind spots. Specialized in bridging the gap between raw cloud compute and bottom-line business metrics, consistently delivering solutions that ensure 100% data fidelity while driving down infrastructure overhead.
Diploma
Account and Business (RQF Level 4)
Professional Certificate
Universal AI
BSc
Accounting (Hons)
IBADAN, OYO, Nigeria
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Full Time
Summary
Advise and lead cross-functional data automation initiatives for enterprise and mid-market clients. Architect and deploy production-grade cloud infrastructure, automated ETL/ELT pipelines, and robust data validation systems that eliminate operational blind spots, secure data integrity, and drive real-time business intelligence.
Highlights
Engineered and deployed containerized ETL/ELT health-check workflows utilizing n8n, Python, and webhook APIs; reduced cross-platform data inconsistencies by 40% by architecting automated validation scripts that scan, match, and flag missing transactional records in real time.
Designed and optimized relational SQL data models and analytical views within cloud environments; mapped complex business logic to power real-time Looker Studio dashboards, giving stakeholders 100% visibility into project performance metrics and resource utilization.
Built automated, API-driven self-service reporting pipelines feeding into decentralized semantic layers in BigQuery; decreased manual data-reconciliation cycles by 30% while establishing a single source of truth for executive stakeholders.
Maintained high pipeline reliability through monitoring, IAM governance, and proactive troubleshooting.
Audit Assistant
IBADAN, OYO, Nigeria
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Summary
Designed data verification frameworks and validation models for complex audit engagements. Specialized in mapping systemic financial data flows, establishing rigorous data reconciliation checks, and ensuring absolute compliance and data fidelity across corporate client accounting structures.
Highlights
Executed large-scale financial data quality audits, developing algorithmic validation rules to pinpoint system anomalies and data inconsistencies; eliminated systemic reporting risks by isolating critical structural control gaps across multi-platform client ledgers.
Built complex, logic-driven data reconciliation engines to ingest and aggregate multi-source transactional data; streamlined end-to-end validation processes, reducing reporting error rates and manual verification cycles through structured data modeling.
Issued By
Issued By
Issued By
Microsoft
Issued By
DeepTech_Ready
n8n, Make, Zapier, Low-Code/No-Code Automation, Webhook APIs, REST APIs, API Integrations, JSON, Cross-Platform System Integration, Shell Scripting/Bash.
IAM Governance, User Access Control, Least-Privilege Management, Data Integrity & Quality Health Checks, Monitoring, Logging & Troubleshooting, Operational Support, Technical Documentation.
ETL/ELT Pipelines, Apache Airflow, SQL, Python, R, Data Modeling, Data Validation, Data Quality Monitoring, Pipeline Orchestration, Workflow Scheduling, Jupyter Notebooks.
Looker Studio, Power BI/Tableau, Dashboard Ownership, Advanced Excel/Google Sheets, Financial & Operational Data Analysis, Executive Reporting.
BigQuery, Snowflake, PostgreSQL, MySQL, dbt (Data Build Tool), Airtable, SQL Server, Data Warehousing Concepts.
Google Cloud Platform (GCP), Microsoft Azure, Linux Administration (Ubuntu), Windows Server, Docker, Containerization, CI/CD Fundamentals, Systems Administration, Git, GitHub, Version Control.
Project
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Summary
- Built a containerized ETL pipeline for ingesting, validating, transforming, and storing review datasets - Implemented producer → broker → orchestration architecture - Stack: Docker, Python, SQL, PostgreSQL, workflow orchestration - Added automated validation checks and monitoring for pipeline reliability
Project
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Summary
The Client Onboarding Workflow is an automation that allows users to quickly streamline their new clients project on boarded. It eliminates repetitive tasks like folder creation, email notifications, and spreadsheet logging—tasks that are time-consuming and prone to error when handled manually.
