Financial Data Officer

Problem

Financial data often sits in Excel, ERP, bank statements, and manual ledgers. That causes inconsistent metric definitions, month-end reports assembled by hand, weak anomaly checks, and slow dashboards. The Financial Data Officer skill treats these as a data-pipeline problem rather than a one-off spreadsheet task.

How it works

• Collection and governance: identify sources, design an ETL flow, handle deduplication, completion, code mapping, and create a star schema with accounting checks.
• Metric system: build a metric dictionary, define formulas at field level, reconcile same-name indicators, and produce a verifiable metric tree.
• BI dashboards: design role-based Power BI views for CFO, business lines, treasury, and budget, write annotated DAX measures, and test interaction and refresh performance.
• Quality monitoring: define completeness, accuracy, consistency, timeliness, and uniqueness rules; score data; alert on amount spikes, duplicates, or account mismatches.
• Automated reporting: rank repetitive reports by frequency, effort, and error rate, then use Power Query, Python, or RPA with exception handling, logging, and backups.

Boundaries

It fits financial data warehouses, management reporting, and BI analysis. It does not replace accounting judgment, tax compliance decisions, or source-system access approvals. The skill assumes explicit metric confirmation, quality gates, and sensitive-data masking; if APIs, Power BI, or warehouse infrastructure are missing, it falls back to Excel or batch processing.