- Developed and deployed a robust, scalable data pipeline in Python and SQL to ingest and transform data from over 1,000 respondents via the SurveyMonkey API, integrating seamlessly with a SQL Server data warehouse. Leveraged REST APIs, SQL, and software engineering best practices to streamline data flow, enabling the Business Analytics team to access faster, reliable insights.- Enhanced SQL data pipelines through advanced SQL development and in-depth data warehouse analysis across 8 tasks, optimizing data workflows and significantly increasing process efficiency.- Led code integration and deployment processes as Build Engineer, managing version control, orchestrating code merges, and deploying builds to SIT, UAT, and production environments through 8 release cycles. Enhanced CI/CD pipeline workflows to ensure efficient and reliable code delivery, leveraging best practices in source control, automation, and release management.- Collaborated with cross-functional teams in an Agile environment, using Azure DevOps to manage sprints, track progress, and ensure alignment on project goals.

- Established the data engineering foundations for a predictive analytics initiative aimed at reducing hospitalization rates among patients with respiratory illnesses by monitoring biomarkers like respiratory and heart rate, helping explore a new line of business in preventative healthcare- Created 3 scalable data pipelines to ingest and process high-frequency biosensor data into BigQuery, leveraging Google Cloud services and APIs with Postman and JavaScript, supporting dashboards and visualizations and driving product development efforts for stakeholders- Guided product strategy by evaluating and recommending biosensors for the final project, researching sensor accuracy, reliability, and data quality to ensure alignment with project goals in predictive healthcare analytics.

Selected Courses: Principles and Techniques of Data Science - Quantitative Research Methods - Experiments and Causality - Applied Machine Learning - Applied Natural Language Processing - Applied Impact Evaluation - Agile Programming Methodologies - Biosensory Computing

- Spearheaded the development of a Python and SQL-based ETL pipeline to ingest complex EHR data from hospitals, addressing challenges of data variability and compliance to deliver high-quality, secure data for clinical analytics and visualization. Achieved a 90% increase in report ingestion speed, empowering faster, data-driven healthcare insights and improving reporting efficiency.- Ingested over 100 electronic health records, populating a database with more than 1 million hospital encounters to provide hospitals with actionable insights into surgical performance and patient outcomes.

- Developed a fraud detection and reporting system using Python, SQL, and Tableau to identify and block fraudulent sources on TubeMogul’s advertising platform, reducing fraud by 50%—well below industry standards. Implemented advanced data analytics techniques to pinpoint suspicious patterns, resulting in substantial cost savings and boosting client trust in platform quality.- Delivered ad hoc reporting, visualizations, and actionable insights on dozens of ad campaigns and business initiatives, using tools like Tableau and SQL to inform strategy and optimize ad performance. - Partnered with marketing, engineering, and business stakeholders to support data-driven decision-making, contributing to improved campaign targeting and strategic alignment across teams.

Built a data product in Python and SQL to analyze and compare Facebook likes and demographic data across user cohorts from multiple marketing campaigns, providing client companies with actionable business intelligence.

- Collaborated with the CEO & CIO to promote a data visualization platform for business managers- Created data visualization demos on Aentropico’s site for potential clients and interested developers using R