Bruno – Python, SQL, AWS

Worked on the development and optimization of advanced data and AI pipelines to support large-scale content processing and analytics for a leading media organization, improving the efficiency and reliability of data flows and enabling better insights from complex datasets.

• Built and maintained scalable data pipelines using AWS Managed Workflow for Apache Airflow (MWAA), AWS S3, Snowflake, and dbt to efficiently manage data ingestion and transformation;
• Collected and managed text data from internal APIs, securely storing initial datasets in AWS S3;
• Leveraged OpenAI APIs to summarize text content, enhancing data value through automated processing and enrichment;
• Established structured data layers in Snowflake, transforming raw data into analytics-ready formats through data modeling, tagging, and aggregation with dbt;
• Communicated key insights and performance metrics to stakeholders through automated email reports, dashboards, and data applications;
• Developed and optimized prompt engineering strategies to improve interactions with AI models, ensuring higher-quality data summaries and enriched content;
• Implemented CI/CD standards and workflows to streamline development and deployment processes.

A project focused on developing and improving data marts for a personal finance platform, aiming to enhance data organization, accessibility, and reliability to support analytics, reporting, and business decision-making.

• Delivered 4 comprehensive Data Marts and developed over 30 complex data models, including tables, views, and macros, utilizing Snowflake, dbt, and Apache Airflow with data sourced from Segment;
• Performed sophisticated data transformations using dbt, ensuring high data integrity, consistency, and accuracy for analytics and reporting;
• Optimized Snowflake database storage and query performance, significantly reducing query times and improving real-time data accessibility for better-informed business decisions;
• Designed and deployed robust, automated data pipelines in Apache Airflow, enhancing operational efficiency and ensuring seamless data flow across various departments;
• Authored detailed documentation and optimized orchestration processes to streamline workflows and enhance system efficiency.

The project focused on building the first regional Data Warehouse for the LATAM market, starting with the Supply Chain domain. The goal was to centralize and streamline data from multiple sources, enabling better visibility into logistics, inventory, and operational performance across countries in the region. This foundation later supported the expansion of data analytics and reporting capabilities for other business areas.

• Developed and implemented comprehensive data migration solutions, transitioning SAP data to Snowflake;
• Built and optimized ETL pipelines to automate data processing, ensuring consistent data availability;
• Designed and maintained interactive Power BI dashboards to provide stakeholders with timely and actionable insights;
• Collaborated with business teams to understand requirements and translate complex business needs into effective data-driven solutions.

The project centered on developing and optimizing data processes for audience segmentation - including data extraction, transformation, and analysis. The main goal was to enable more accurate audience profiling and improve targeting strategies through data-driven insights and advanced analytics.

• Executed targeted analytics and audience segmentation projects using data from Google Ad Manager;
• Created detailed reports and interactive dashboards utilizing Google Data Studio and Google Cloud Platform;
• Leveraged Google BigQuery for efficient data processing and querying, enabling rapid insights for marketing and content strategy decisions;
• Facilitated cross-team collaboration to ensure alignment of data solutions with strategic business objectives.