Carolina Espinoza

• ● Automated the metadata generation process for a critical database, reducing the manual processing time by 90% and ensuring data integrity by designing and deploying LLM-based agents (AI agents).

• ● Engineered the underlying data infrastructure for critical business dashboards by creating robust tables in BigQuery using advanced SQL and defining complex LookML measures and views to support consistent, scalable, and governed metrics.

• ● Leveraged Python to manipulate and analyze large datasets, delivering actionable insights that influenced strategic business decisions and increased operational efficiency.

• ● Developed interactive dashboards and visualizations in Power BI, enabling real-time data tracking and enhancing stakeholder decision-making processes by providing clarity and immediate access to critical data.

• ● Collaborated with cross-functional teams to gather requirements and deliver reports, leading to a reduction in report generation time, allowing teams to focus on product development and operational performance.

• ● Improved data quality metrics across departments by implementing data quality controls to enhance the accuracy and reliability of insights.

• ● Presented complex data findings to diverse audiences, translating technical information into actionable insights that fostered collaboration and informed strategic initiatives.

• ● Improved data transparency and collaboration among stakeholders within the procurement department by developing interactive data visualizations using Power BI, Python, and SQL.

• ● Boosted operational efficiency by creating reports and dashboards to provide data insights focused on enhancing data-driven decision-making and streamlining operational processes.

• ● Reduced data discrepancies by conducting data quality assessments to ensure accuracy and consistency, and support compliance with internal data governance standards.

• ● Drove performance improvements by collaborating with cross-functional teams to define key performance indicators (KPIs) while aligning data analysis efforts with organizational goals.

• ● Automated reporting processes to enable more timely insights and decision-making that positively impacted project timelines.