Enterprise / contribution reconciliation

401(k) reconciliation automation

Designed automation to aggregate and reconcile monthly 401(k) contribution totals across 30 source files.

Monthly aggregation and reconciliation automated

Business question: How could thirty monthly source files be totaled and reconciled without repeating the arithmetic by hand?
My role & scope: Workflow designer and analytics developer
Consequential contribution: I built the file-to-reconciliation flow using Azure storage, Synapse, SQL, and Python, replacing repeated manual calculations.
Outcome: Monthly contribution aggregation and reconciliation ran automatically across 30 source files, replacing manual calculations.

01 / Judgment

The problem and the choice

Each month, a person calculated 401(k) contribution totals from 30 source files and reconciled them. The repeated arithmetic made the process dependent on manual work.

Automate aggregation and comparison in one recurring workflow, then make balanced totals and differences visible in the report.

02 / Delivery responsibility

What I owned

I designed the workflow around files landing in Azure Blob Storage. Synapse and SQL handled transformation, aggregation, and comparison; Python supported validation of the result.

Translated the recurring manual calculation into a workflow covering 30 monthly source files.
Built aggregation and comparison logic in the Azure analytics environment.
Used Python to support result validation and surfaced balances or differences in the reconciliation report.

03 / Working with others

How the work moved forward

My responsibility was the analytical workflow: translating recurring reconciliation work into aggregation, comparison, and reporting.

04 / Technical detail

The implementation decisions

File intake: Monthly contribution files landed in Azure Blob Storage before transformation.
Aggregation and comparison: Synapse and SQL transformed the inputs, calculated contribution totals, and compared those totals for reconciliation.
Operational output: The report showed whether totals balanced and exposed differences when present. That is workflow behavior, separate from establishing calculation accuracy.

Thirty monthly contribution files flow into Blob Storage, through Synapse and SQL aggregation, to reconciliation and a balance report. Python supports result validation. — **Public reconstruction.** This sanitized reconstruction explains the data flow and report outputs. It contains no retirement records and is not a validation test report. Open full-size diagram ↗

05 / Validation

Validation evidence

Python supported validation of the aggregation and reconciliation result. The original validation cases and acceptance criteria are not included in this public account; the balance and difference states in the diagram describe system behavior.

06 / Outcome & status

What changed

Monthly contribution aggregation and reconciliation ran automatically across 30 source files, replacing manual calculations.

This case covers the monthly contribution calculation and reconciliation workflow across 30 source files.