JACOB GARRETT

• Led cross-platform cloud optimization initiatives, reducing Snowflake warehousing costs by 15% through workload restructuring and automated shutdowns, while simultaneously improving efficiency by 20% via strategic warehouse adjustments and notifications
• Architected and deployed a comprehensive cloud-native data pipeline using Prefect on Google Cloud, leveraging Big Query and Cloud Run, later expanding the architecture to incorporate Snowflake and GIS databases for enhanced geospatial data processing
• Spearheaded the rearchitected of Enterprise Data Warehouse, implementing a clear and robust design that streamlined schema change processes for 5 teams, ensuring 100% data consistency and reducing errors by 30%
• Pioneered the development of a Python Docker microservice for real-time monitoring and adjustment of Snowflake costs, resulting in monthly savings of $10,000, while also enhancing Azure DevOps pipelines with Python and Azure CI/CD in PLF framework, increasing efficiency by 15%
• Implemented critical enhancements across multiple cloud platforms, including optimizing AWS Sage Maker workflows, utilizing Snowflake (SQL and Python) for problem identification, and leveraging Terraform to manage Google Cloud Storage, collectively improving data storage efficiency by 25% and saving 50 hours of development time.

• Led the overhaul and optimization of Amply Media data pipelines, orchestrating migration to Snowflake, Airflow, and AWS, resulting in a 30% improvement in overall system efficiency and seamless integration with the Sonobi’s data infrastructure
• Spearheaded the implementation of advanced Snowflake features, boosting data processing efficiency, pipeline performance, while deciphering and optimizing thousands of lines of legacy SQL code to service critical revenue dashboards
• Architected and implemented a comprehensive testing protocol for data from 10+ API sources, ensuring 99% data quality and lineage throughout the integration process, crucial for maintaining accuracy in revenue reporting
• Pioneered the adoption of AWS Lambda, converting 20 Python scripts into event-driven processes, enabling real-time data handling from website interaction of user behavior data into the new system.

MUFG Union Bank (Service Team)

• Developed an innovative ELT solution using Python and Azure services, generating labor cost saving of over $100,000 semi-annually and reducing time-to-insight for executives, directly enhancing leadership decision-making capabilities
• Led quality assurance and architectural design for multiple projects within the data migration initiative, maintaining rigorous standards that reduced errors, while mentoring three developers to foster a culture of excellence
• Designed and refined 12 Tableau dashboards, leveraging Advanced Data Modeling Techniques, advancing data visualization capabilities for production teams and enabling data-driven decision-making.

Enterprise Products, Big Data Team

• Created Flask web application for data validation and power optimization, incorporating Dimensional Modeling to enhance data integrity, resulting in a 10% reduction in data errors
• Generated the design and optimized ETL pipelines using Alyteryx and Apache Nifi with Jenkins for Test and Deploy phases, helping to boost pipeline performance by 2% and enhance data processing capabilities
• Developed PI WEB API application on Azure using Python, improving Apache Influx Database data extraction efficiency by 5% and enhancing data handling accessibility
• Assisted in implementing Hadoop MapR and Apache Spark for managing 500 GB of data, contributing to a 3% improvement in query response time for big data processing.

• Streamlined data processing for faster results by implementing advanced analytics tools
• Presented findings to executive leadership teams through concise presentations, influencing future strategy development
• Integrated multiple sources of disparate data into cohesive datasets using ETL processes, improving overall analytic capabilities
• Provided actionable insights through comprehensive reports and dashboards, supporting strategic initiatives.

• Spearheaded the migration of legacy systems to more scalable and maintainable Python-based solutions
• Increased code reusability with modular programming techniques, facilitating faster development cycles
• Managed database security by implementing appropriate access controls, maintaining compliance with industry regulations and company policies
• Automated routine maintenance tasks through scripting, freeing up valuable time for higher-priority projects
• Integrated multiple sources of disparate data into cohesive datasets using ETL processes, improving overall analytic capabilities.