I’m a Data Engineer with 4 years of experience building, modernizing, and supporting enterprise data solutions. I began my career in software development at General Motors, where I worked on SQL-based solutions, reporting applications, data validation, and enterprise workflows before transitioning into Data Engineering.
In my Data Engineering role, I worked extensively with SQL, Python, PySpark, Databricks, Apache Spark, Delta Lake, Oracle, Hive, and ETL/ELT development. A major part of my work involved migrating approximately 12 enterprise ETL pipelines from legacy Oracle systems to Databricks, redesigning SQL and PySpark transformations, validating migrated results, and improving pipeline performance. Many of these workloads went from roughly 4 hours of processing time to 1.5–2 hours after migration.
I also have experience with Databricks Workflows, job scheduling and dependencies, data quality validation, production monitoring, root-cause analysis, troubleshooting, data lineage, technical documentation, Git/GitHub, GitHub Actions, Azure DevOps, CI/CD, and deployment automation. I regularly partnered with business stakeholders, reporting teams, and downstream data consumers to translate requirements into technical solutions and ensure reliable data delivery.
I’m especially interested in remote opportunities in Data Engineering, Analytics Engineering, Data Platform, Data Quality, Data Analytics, Business Intelligence, Data Migration, and other closely related technical roles. I enjoy solving complex data problems, improving reliability and performance, modernizing legacy systems, and finding practical ways to make data more useful for the people and teams who depend on it.
I’m open to working with US-based, international, and globally distributed teams, and I’m also open to relocation opportunities. I’m particularly interested in companies where I can continue developing my technical skills while contributing to meaningful products, modern data platforms, and collaborative engineering teams.
