David Jose Giraldy

🔴 Architected and implemented end-to-end data pipelines using AWS Glue (PySpark), adopting the Medallion Architecture (Bronze, Silver, Gold) to ensure data quality and structured storage in Amazon S3. 🔴 Optimized ETL processes by transforming raw unstructured data into business-ready insights in Amazon Redshift, improving query performance for the BI team. 🔴 Automated Data Orchestration and monitoring using AWS Step Functions and CloudWatch, reducing manual intervention and ensuring 99.9% pipeline uptime. 🔴 Implemented Data Cataloging and Governance using AWS Glue Data Catalog, enabling seamless data discovery and ad-hoc analysis via Amazon Athena. 🔴 Architected an Interoperable Data Lakehouse solution by integrating Apache Iceberg on Amazon S3 as external storage, testing Snowflake's capability in handling open-table formats and cross-platform data consistency. 🔴 Engineered complex Data Orchestration using Snowflake Task DAGs, automating end-to-end pipelines and ensuring robust dependency management within a native warehouse environment. 🔴 Conducted Deep-Dive Performance Optimization, leveraging clustering strategies and warehouse profiling to achieve the optimal balance between query speed and credit consumption for high-volume workloads.

🔴 Collected and integrated data from diverse sources including Database systems, SAP, and Excel. Performed data transformation and processing using Talend Management Studio (ETL tools), SQL Server Management Studio (SSMS), and SQL Server Analysis Services (SSAS). Designed and delivered insightful data visualizations and dashboards using Power BI to support business decision-making. 🔴 Collaborated cross-functionally with Finance, Accounting, Export Import Division, Internal Audit & Risk Management, Business Operations, Supplier Network Division, and Spare Part Division to develop descriptive and predictive analytics solutions (e.g., Info Record Analysis, Purchase Order Analytical, and Cost Accounting Digitalization support). 🔴 Conducted business intelligence analysis to identify trends, uncover business opportunities, and provide actionable recommendations. Developed automated reporting and monitoring systems to improve operational efficiency and reduce manual workload. 🔴 Utilized SQL and Python to create data models, automate processes (e.g., auto-email reminders, data quality checks), and enhance data reliability. Communicated analytical insights through compelling dashboards and presentations tailored to stakeholders' needs.

🔴 Performed Data Cleaning, Preprocessing, and Analysis of the Credit Scoring Dataset. 🔴 Analyzed large amounts of Information to search and discover trends and patterns in the data. 🔴 Build predictive Machine Learning models with various algorithms (Logistic Regression, Gradient Boosted Trees, SVM, K-Nearest Neighbors, Gaussian, Neural Network). 🔴 Tuning the model to achieve better results using Optuna. 🔴 Create Pipeline on Final Model for API Development using Flask.