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Daniela Nunez

@danielanunez

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Data Engineer at Cotiviti who deployed an AI-assisted mapping tool that cut manual mapping time by about 50%.

United States

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At Cotiviti, I build production SQL and PySpark ETL workflows in Databricks to transform and standardize large-scale health insurance claims datasets.

I deployed an AI-assisted data mapping tool that predicts matches between raw client fields and proprietary schemas, cutting manual mapping time by about 50% and approximately doubling mapping efficiency.

At Emanate Biostatistics, I prepared, cleaned, and validated clinical trial datasets with SQL, delivering regulatory-grade, analysis-ready data for pharmaceutical studies.

At San Diego State University Center for Artificial Intelligence, I ran Python machine learning analyses on brain metric datasets and presented findings to psychiatrists. I hold a Bachelor of Science in Statistics from San Diego State University and am SAFe Agile Certified.

Experience

Work history, roles, and key accomplishments

Current

Data Engineer

Current

Cotiviti

Nov 2024 - Present (1 year 11 months)

Build production SQL and PySpark ETL workflows in Databricks to transform and standardize large-scale health insurance claims datasets. Perform data profiling, quality validation, reconciliation, and root-cause analysis across multi-million-record datasets.

SQL PySpark Databricks ETL Data Profiling Data Quality Root Cause Analysis Claude Code AI assisted Development

Statistical Programmer

Emanate Biostatistics

Jun 2023 - Nov 2024 (1 year 5 months)

Prepared, cleaned, and validated clinical trial datasets using SQL, delivering regulatory-grade, analysis-ready data for pharmaceutical studies. Performed data quality checks and resolved discrepancies with biostatisticians and study teams.

SQL Clinical Data Data Cleaning Data Validation Pharmaceutical Sciences

AI Research Intern

San Diego State University Center for Artificial Intelligenc

Apr 2022 - Jun 2023 (1 year 2 months)

Designed and ran machine learning analyses in Python on brain metric datasets, using PCA and clustering to identify patterns and correlations in patient data. Created data visualizations and presented findings to psychiatrists.

Python Machine Learning PCA Clustering Data Visualization scikit learn