Financial Data Analyst combining accounting expertise with SQL, Python, and Power BI to build reliable financial analysis and reporting systems.

Chartered Accountant and Financial Data Analyst with over 10 years of experience across finance, accounting, and analytics, including:

• Financial analysis, budgeting, forecasting, and variance analysis
• Reporting automation and process optimisation
• Data-driven insight delivery for planning and performance management
• Audit-ready systems, controls, and compliance frameworks My work sits at the intersection of finance, data engineering, and analytics, with a focus on accuracy, traceability, and decision quality.

I help organisations move from static reporting to insight-driven decision making using Python, SQL, and data engineering best practices.

I designed and built FDA Toolkit an enterprise grade Python toolkit for financial analysts accountants and data professionals who need reliable auditable and repeatable analytics.

An enterprise-grade Python toolkit designed for financial analysts, accountants, and data professionals who need reliable, auditable, and repeatable analytics. FDA Toolkit delivers 67+ production-ready functions for financial data cleaning, validation, profiling, and pipeline automation, helping teams replace fragile spreadsheets with controlled, scalable workflows.

Install:

pip install fda-toolkit

Why FDA Toolkit Matters:
Most finance teams struggle with inconsistent data, manual checks, and reporting processes that do not scale. FDA Toolkit reflects how I approach financial analytics in practice: build once, reuse safely, and trust the output. The toolkit embeds financial controls, validation logic, and traceability directly into the analytics layer, ensuring results remain dependable as data volume and complexity grow.

Key Features:

• 67 production-ready functions across 8 intelligent modules
• Full type hints with IDE autocomplete throughout
• Compliance-ready with automatic audit logging & traceability
• Finance-aware validation for real-world workflows
• One-line pipelines for complex transformations (e.g., ftk.quick_clean_finance())
• Enterprise quality — error handling, security, memory optimization

📌 View on PyPI • GitHub Repository

💡 Philosophy: Good data engineering makes good analysis simple. Clean data, reliable pipelines, audit trails built in.