Projects

• Profiled 50+ financial datasets to quantify null rates, duplicates, schema drift, and data completeness.
• Built exploratory analysis and summary reports to expose data quality issues to business and analytics teams.
• Standardized metric definitions and dataset documentation to align reporting across teams.
• Created dashboards used in sprint and leadership reviews to track data readiness and risks.

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• Converted real defect tickets into data validation tests to verify fixes and prevent regressions.
• Built before-and-after dataset comparisons to confirm pipeline changes behaved as expected.
• Validated downstream tables impacted by upstream fixes to ensure reporting accuracy.
• Produced validation reports used by engineering and QA teams before production release.

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• Designed a reconciliation workflow to normalize and match 50+ multi-million-row datasets.
• Applied deterministic matching rules and exception handling to achieve ~95% matching accuracy.
• Tracked 200+ reconciliation items in a centralized control sheet for operational visibility.
• Generated stakeholder reports that increased reconciliation completion by ~10% and reduced errors by ~30%.

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• Built SQL and Python checks to validate incoming fraud signals and model input data.
• Detected missing, delayed, and anomalous records before they impacted model scoring.
• Implemented production alerts to notify teams of data quality and pipeline failures.
• Maintained tracking and reporting to support weekly risk and operations reviews.

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