I build trust layers for financial data.
I’m drawn to the moment when someone says “the data doesn’t make sense.” That’s where I do my best work. I turn ambiguity into clarity so teams can see what’s going on, why it matters, and what to fix next.
What I deliver
Regulatory grade reconciliation at scale
Standardize identifiers, match records, and track exceptions across many upstream systems so discrepancies are visible, owned, and auditable instead of lost in manual trackers.
Data quality controls that prevent regressions
Validation rules, automated checks, and release ready test cases that catch issues early and keep financial reporting stable as pipelines evolve.
Analytics ready datasets stakeholders trust
Clear metric definitions, documented logic, and governed datasets that let finance and operations teams answer questions without debating what the numbers mean.
Featured case studies
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Large Scale Financial Data Reconciliation
How I improved matching accuracy and reduced discrepancies across multi million row financial datasets in a regulated environment without exposing client details.
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Case study
Fraud and Risk Data Pipeline Reliability
How I strengthened data quality checks, monitoring, and incident response for a production fraud and risk pipeline in a regulated financial environment.
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