Case study
Data Governance & Data Profiling for Financial Reporting
Built a data governance and profiling foundation that made financial reporting trustworthy, explainable, and auditable.
Context
Multiple finance and operations teams were using the same datasets to drive reporting and decisions, but no one could clearly explain where the numbers came from, what they meant, or whether they were correct.
As a result, analysts spent more time reconciling numbers than analyzing them, and reporting cycles were slowed by repeated questions and last-minute fixes.
The problem
- Different teams were producing different answers to the same financial questions
- Data quality issues were discovered only after reports were already in use
- No clear ownership of tables, metrics, or definitions, creating audit and compliance risk
What I built
A lightweight data governance and profiling system that made dataset quality, ownership, and definitions visible before issues reached reporting and leadership.
- Automated profiling of row counts, nulls, duplicates, and value distributions to surface issues early
- Business-friendly EDA summaries that translated technical findings into financial context
- Standardized documentation for tables, fields, and financial metrics to align teams
- Status dashboards used in sprint planning and leadership reviews to drive accountability
Impact
- Reduced ambiguity around dataset quality, ownership, and metric definitions
- Improved confidence in financial and operational reporting
- Shortened onboarding time for analysts and engineers
- Lowered the risk of reporting errors reaching leadership or external stakeholders
Why this matters in interviews
- Shows how I design data quality, controls, and governance in real financial systems
- Demonstrates how I bridge engineering, analytics, and business stakeholders
- Reflects how I build systems that reduce risk and increase trust at scale