A curated view of the problems I have helped define, engineer, validate, and turn into business action. Professional work is described at a sanitized level; public technical examples use synthetic data only.
These four projects best represent my current mix of technical depth, business ownership, stakeholder trust, and measurable outcomes.
Trusted Databricks-based logic that explains processing failures, supports USPS assessment review, and drives measurable financial improvement.
Read the case study →End-to-end analytics built from business-rule design through SSIS, SQL Server, Power BI, stakeholder validation, rollout, and user support.
Read the case study →Governed fraud and financial-risk analytics developed in Databricks, refined through management and executive review, and designed for Power BI.
Read the case study →A specialized component of enterprise reconciliation that turns raw application logs and payload text into reusable root-cause classifications.
Read the case study →These case studies show the range behind the headline results: data migration, product evolution, inventory analytics, lineage, ETL, technology strategy, and operational implementation.
Bridging legacy SQL reporting and future-state architecture through Confluence documentation, Jira/Agile delivery, migration QA, and Power BI validation.
Read the case study →A quality and service-risk solution shaped from a high-level business need and evolved from automated Excel reporting to Tableau and Power BI.
Read the case study →Multi-source inventory reporting that improved visibility into purchased, rented, on-hand, inbound, outbound, and customer-issued supplies.
Read the case study →Mapping reporting architecture and dependencies exposed unused objects, technical debt, and pipeline optimization opportunities.
Read the case study →Historical data engineering for a last-mile delivery proof of concept using Bringg/FarEye exports, SSIS, SQL Server, SFTP, and Tableau.
Read the case study →A cost-benefit and technology evaluation that helped leadership choose Power BI rather than SSRS as the future BI direction.
Read the case study →A business case and leadership presentation that supported the move from SQL Server 2008 R2 to SQL Server 2017.
Read the case study →Hands-on process improvement, supervisor training, and quality-control knowledge transfer at another distribution center.
Read the case study →I keep planned or learning-focused work clearly separate from production experience. The current lab roadmap is intentionally focused: one integrated Python/Snowflake/dbt analytics capstone, practical statistical analysis with Python, SQL performance tuning, and a public Power BI demonstration—all using synthetic or public data.