Built data pipelines in Python and AWS S3, adding automated recovery and alerts that cut manual fixes by 80% and removed weekend shifts. Made delivery 2x faster and boosted team autonomy by designing internal tools with Django that let teams create and manage their own pipelines. Created processes for data cleaning, replication, and movement between databases using Snowflake and SQL Server. Improved performance and memory use in analytics workflows by processing large datasets with Pandas and Polars. Created transformations with dbt to keep the data lineage and the versioning of the data with Snowflake. Used Rundeck and Airflow as workflow orchestrators.

Redesigned SQL Server databases by separating transactional and analytical layers, improving performance and enabling richer client metrics. Built a data warehouse and ETL pipelines with Spark, making report generation 3x faster for over 4 million GPS devices. Increased dashboard usage by 60% by enhancing reporting and visualization tools with Java and .Net. Supported real-time tracking for government and corporate clients by integrating push notifications in GCP.

Reduced operational costs and fuel use by 35% with a centralized data system built on SQL Server. Cut manual work by 60% by building Python pipelines to process and consolidate offline CSV data. Automated data replication between SQL Server and Oracle using custom ETL scripts. Improved data quality by using Git for version control and adding validation checks during ingestion.