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Senior Analyst with 10+ years in Financial Data Analysis
Detail-oriented finance professional with 13+ years of progressive experience in financial reporting, budget management, payroll processing, and audit support across a public sector research institution. Currently making a deliberate transition into data analysis, building hands-on skills in Excel (advanced), SQL, Power BI, and Python. Brings rare domain expertise — budget variance analysis, reconciliation, payroll integrity checks, and executive reporting — that most data analyst candidates develop only after years on the job. Seeking opportunities to apply this combined finance and data skill set in analytical roles across any sector or geography.
The Polytechnic, Ibadan
National Diploma (ND) · Accountancy
N/A – June 30, 2007
The Polytechnic, Ibadan
Higher National Diploma (HND) · Accounting
N/A – June 30, 2011
Forestry Research Institute of Nigeria (FRIN)
Senior Finance Officer
January 1, 2018 – Present
India
Forestry Research Institute of Nigeria (FRIN)
Finance Officer
April 1, 2015 – December 1, 2017
India
Forestry Research Institute of Nigeria (FRIN)
Accountant
April 1, 2012 – April 1, 2015
India
Forestry Research Institute of Nigeria (FRIN)
Accounts Executive
November 1, 2008 – April 1, 2012
India
Monthly Budget vs. Actual Expenditure Dashboard
January 1, 2024 – December 31, 2024
• Built a two-sheet Excel workbook with SUMIF-driven formulas, variance analysis (amount and %), conditional formatting for budget breaches, and a dynamic bar chart - auto-refreshes with new data. • Designed as a reusable monthly reporting template applicable to any organisation's budget cycle.
SQL Payroll Audit Query Pack
January 1, 2024 – December 31, 2025
• Designed 6 reusable SQL queries to detect payroll anomalies: unregistered payees, duplicate payments, unapproved high-value transactions, and monthly spend spikes. Documented each query with plain-English comments for use by non-technical audit staff. Tools: SQL (SELECT, JOIN, GROUP BY, HAVING, subqueries), DB Browser for SQLite.
Retail Dataset Quality Audit & Descriptive Statistics Report
January 1, 2024 – December 31, 2024
• Conducted a full data quality audit on a retail orders dataset - resolving duplicates, outliers, formatting errors, and missing values; documented every decision with business justification. • Applied descriptive statistics (mean, median, standard deviation, percentiles) to summarise order patterns and flag anomalies. Tools: Excel, statistical functions, conditional formatting.
Data Analytics Programme
Unknown
June 1, 2026 – Present
Google Data Analytics Certificate
June 1, 2026 – Present
AI Software Development Bootcamp
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's long tenure at a single institution (Forestry Research Institute of Nigeria) suggests loyalty and stability. Their deliberate transition into data analysis, coupled with ongoing certifications and personal projects, indicates a strong growth mindset and self-motivation. The focus on financial data and audit aligns well with roles requiring precision and adherence to standards. The candidate's value proposition emphasizes bridging the gap between technical tools and real-world financial data constraints, suggesting a practical, results-oriented approach. The public sector background might imply a preference for structured environments, but the pursuit of new skills shows openness to change and new challenges.
Soft Skills & Operational Fit
The candidate demonstrates strong attention to detail, a methodical approach to data quality and auditing, and a clear understanding of financial regulations and compliance. Their experience in leading budget preparation and monitoring, producing financial reports, and overseeing payroll processing indicates strong organizational and analytical skills. The ability to document SQL queries for non-technical staff suggests good communication and collaboration potential. The candidate's proactive pursuit of data analytics certifications and projects indicates a strong drive for continuous learning and adaptability, which are crucial for operational fit in a data-driven role.