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Daniel Izquierdo
Mid-level Data Analyst specializing in financial risk and data automation
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Analytics professional from Capital One with strong experience automating risk, reconciliation, and regulatory reporting workflows in financial services. They combine deep SQL/Python pipeline skills with stakeholder-facing dashboard and KPI design, delivering measurable impact like 30% performance gains, sub-24-hour anomaly detection, and 100% data integrity for regulatory filings.
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Key Strengths
- Built SQL-based reconciliation automation for messy financial/legal transaction data
- Strong data quality and logic normalization expertise across mismatched source systems
- Improved query performance by 30% through partitioning on indexed date ranges
- Reduced manual case volume to one third via exception-based reporting
- Saved hundreds of agent hours per month through audit automation
- Built reproducible Python analytics pipelines in Databricks using pandas and PySpark
- Implemented validation gates for schema changes and unexpected nulls
- Successfully drove stakeholder trust by parallel-running automation against manual audits
- Owned end-to-end risk dashboard delivery from stakeholder alignment to rollout
- Cut time to detection from 30 days to under 24 hours
- Achieved 100% data integrity for monthly regulatory filings
- Designed actionable business KPIs such as autopay enrollment with edge-case handling
- Refined metrics with segmentation logic to support product and campaign analysis
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