• Created and maintained a materialized view in BigQuery to process transaction data for real-time analytics, enhancing data accessibility and decision-making. • Developed a data quality validation component using Dataplex to monitor and address incorrectly processed payments, ensuring data integrity and accuracy. • Used Airflow and Cloud Composer to orchestrate the extraction of data from Google Analytics, resulting in a 35% decrease in processing time. • Optimized stored procedures by implementing partitions and clusters, resulting in a 60% reduction in costs and a 70% decrease in processing time.

• Designed and implemented a serverless ETL pipeline using AWS Glue and Athena to efficiently process and query CSV files stored in S3 for ad hoc analysis about bank account statements, reducing data retrieval time by 40%. • Used AWS SNS and Lambda functions to process and send data to Redshift, enabling real-time analytics and enhancing data processing efficiency. • Developed and optimized an ELT pipeline using DBT and Snowflake, improving data ingestion, load and transformation efficiency by 30% while ensuring seamless integration with cloud-based analytics workflows. • Orchestrated and automated a data pipeline using Apache Airflow and AWS (MWAA), optimizing workflow scheduling and reducing processing time by 25%. • Utilized Azure Data Factory and Synapse to process and store data from bank client information, improving data integration and analysis efficiency by 35%.

• Queried a database to extract and analyze bank transaction data, improving fault detection and response time by 70%. • Created an interactive Tableau dashboard to display data trends over the years and calculate payback periods per customer, enhancing data visualization and boosting customer satisfaction. • Built an ETL pipeline using PySpark and Databricks to extract and transform data from the Dominican Republic’s Social Security and Pension System, ensuring 100% data availability and improving ROI calculations per customer site by 40%.