Step 01
Designed a stable SQL extraction layer from SAP that returns a consistent structure every run.
CASE STUDY · FINANCE SYSTEMS · SELECTED OUTPUT
FP&A Month-End Reporting Automation
Automated the month-end management reporting pipeline — SQL extraction, Python validation, Power BI delivery.
BeforeMulti-daymanual assembly
AfterSame-dayreconciled delivery
~20 hours / month of manual consolidation removed
01 / CONTEXT
The operating constraint
Month-end management reporting required several days of manual data gathering from multiple sources each cycle — extraction by hand, spreadsheet reconciliation, and re-keying into the reporting pack.
02 / SYSTEM
How the system was rebuilt
Step 02
Added a Python transformation layer with validation checks before anything reaches the pack.
Step 03
Delivered the management pack in Power BI so budget owners see reconciled numbers on demand.
03 / CONTROLS
Systems & stack
PythonSQLSAPPower BI
04 / RESULT
What changed
The reporting cycle moved from multi-day assembly to same-day availability. Roughly twenty hours per month of manual consolidation disappeared, with consistent numbers for budget owners.
EXHIBITS
Supported evidence
FIGURE 01 / CYCLE_TIME
Reporting cycle
Before and after automation
DAYS
▲ FAVORABLE · multi-day assembly → same-day availability
| Label | Value | State |
|---|---|---|
| Before | 4.0 | adverse |
| After | <1 | favorable |
FIGURE 02 / MANUAL_HOURS
Manual consolidation removed
Approximate monthly effort no longer spent assembling the pack
HOURS / MONTH
05 / REUSE
What I would carry forward
Automate reconciliation before presentation. A dashboard on unreconciled data only accelerates confusion.
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