At Milwaukee Tool, I work on cloud-native solutions for enterprise clients, focusing on optimizing large-scale graph databases and building serverless architectures. My work involves improving system performance, reducing infrastructure costs, and enabling real-time analytics on massive datasets.
Key Contributions
- Problem: Gremlin queries on an AWS Neptune graph with 9M+ nodes were running slowly, impacting real-time analytics
- Action: Profiled slow-running traversals, rewrote inefficient queries, and optimized data modeling
- Result: Improved query execution time by 30%, enabling faster insights for enterprise clients
Implemented Test-Driven Development (TDD) Practices
- Problem: Legacy code lacked comprehensive test coverage, leading to frequent regressions and unreliable deployments
- Action: Adopted TDD methodology—writing tests before implementation—for all new features and refactoring efforts. Created unit tests, integration tests, and contract tests for Lambda functions, Step Functions, and API endpoints
- Result: Achieved 85%+ test coverage, reduced production bugs by 45%, and accelerated development cycles with faster feedback loops
- Problem: Processing terabytes of data was slow and inefficient
- Action: Developed ETL pipelines in Go and AWK for data search and transformation
- Result: Reduced data processing time by 50% and improved overall data pipeline efficiency
Impact Summary
- 30% faster query performance
- 85%+ test coverage achieved
- 45% reduction in production bugs
- 40% fewer deployment errors
- 50% faster data processing