Data Platforms
Northstar
Cloud architecture, ingestion, transformation, orchestration, security, infrastructure, and operational data engineering.
Projects
Different systems, built around the same engineering principles.
My engineering work spans cloud data platforms, analytics, enterprise data systems, backend development, integrations, and independent software products. Together, these projects show different parts of the same approach: understand the domain, model the problem carefully, automate what should be repeatable, and build systems that remain understandable when the real world gets complicated.
Product Engineering
ShimStack
Application architecture, domain modeling, workflow design, offline reliability, and end-to-end product development.
Backend, Analytics & Automation
Nessie
Java backend engineering, reusable analytics, diagnostics, data quality, integration, and workflow automation.
Enterprise Data & Analytics
Centralist
Oracle PL/SQL, analytics, enterprise data, production support, and business-to-technical translation.
Flagship Data Engineering ProjectIn Active Development
Northstar Data Platform
A production-style AWS data engineering platform built around a synthetic outdoor-retail company. Northstar is being developed incrementally to demonstrate the lifecycle of a modern data platform rather than a collection of isolated tutorials or disconnected service demos.
What this demonstrates
Hands-on cloud and data-platform engineering, architecture, pipeline design, data quality, testing, observability, security, infrastructure as code, and the ability to explain and defend technical decisions as the platform develops.
Target Platform Scope
The architecture is being implemented in stages. The target platform includes:
- Batch, API, database CDC, and streaming ingestion
- S3 raw, quarantine, and curated data layers
- Glue, PySpark, Athena, Redshift, and dbt
- Apache Iceberg lakehouse architecture
- Orchestration, data quality, and observability
- Terraform, Docker, CI/CD, IAM, and security
- Public portfolio evidence and controlled live-demo access
Independent Software
Product engineering beyond the data platform
Independent product work introduces a different engineering constraint: instead of solving only the technical problem, I also have to decide what the product should do, how users should understand it, how the domain should be modeled, and which complexity should never reach the user.
Independent ProductIn Active Development
ShimStack
An offline-first field-service application designed for owner-operators and small service businesses. ShimStack connects estimating, jobs, visits, scheduling, materials, purchasing, expenses, payments, and business financial planning in one operational workflow.
What this demonstrates
Product architecture, domain modeling, application development, offline-first design, and the ability to turn complicated real-world workflows into understandable software.
- React Native
- TypeScript
- Expo
- Expo Router
- SQLite
- Domain Modeling
- Mobile Architecture
- Offline-First Design
Engineering Highlights
- Designed the product architecture and interconnected field-service workflows from the ground up
- Built with React Native, Expo Router, TypeScript, and local SQLite storage
- Designed around offline-first, single-device reliability for work in the field
- Connected estimates, jobs, visits, work progress, materials, purchasing, and financial tracking so information can flow through the business instead of being repeatedly entered
- Designed cash-bucket planning for taxes, overhead, reserves, and profit
- Built workflows for scheduling, time tracking, mileage, photos, receipts, inventory, purchasing, and material usage
- Designed for eventual commercial distribution through mobile app stores
Product Screens
ShimStack in practice
These screens show several of the core operational areas of the application as they currently exist during active product development.
Home
Daily work, attention items, and upcoming activity.
Customers
Customer records and the starting point for client work.
Jobs
Active work, project status, and field-service operations.
Schedule
Planning visits and managing upcoming workload.
Professional Engineering
Systems built in production environments
These systems were developed as part of my professional work. Proprietary source code, customer information, and internal data are not published here. The descriptions instead focus on my engineering role, technical responsibilities, architecture, and the problems each system addressed.
Professional SystemPrimary Architect & Engineer
Nessie
A Java-backed internal analytics, reporting, diagnostic, and data-quality platform built to support ERP consulting engagements. Nessie processed enterprise data and produced structured JSON outputs that were consumed by a vanilla JavaScript frontend to generate a static analytics site refreshed daily. The interface surfaced financial, project-performance, configuration, compliance, and data-quality findings and allowed users to generate preformatted remediation files compatible with Unanet's import functionality.
What this demonstrates
Backend and frontend software engineering, analytics engineering, enterprise data integration, reusable tooling, automated data-quality workflows, JSON-based application architecture, technical ownership, and engineering for measurable business impact.
Engineering Highlights
- Served as primary architect and engineer for a reusable internal application rather than a collection of one-off reports and scripts
- Developed and maintained Java backend functionality supporting analytics, diagnostics, validation, reporting, and reusable engineering workflows
- Generated structured JSON output that was consumed by a vanilla JavaScript frontend to render a static analytics site refreshed daily
- Built an end-to-end data-quality workflow that not only identified issues but also helped users correct them through preformatted export files compatible with Unanet import processes
- Automated recurring analytical, diagnostic, validation, reconciliation, and reporting work across customer environments
- Standardized repeatable analytical and diagnostic logic so findings were more consistent, traceable, and explainable
- Integrated SQL, Java, JavaScript, JSON, Python, PowerShell, REST APIs, and reusable engineering tooling
- Contributed to automation that saved hundreds of consultant hours annually
- Java
- JavaScript
- JSON
- SQL
- Python
- PowerShell
- REST APIs
- Data Quality
- Analytics Engineering
- Backend Engineering
- Automation
Professional SystemProduct Owner & Developer
Centralist
A centralized marketing data application built on Ellucian Banner data to generate, manage, and deliver institutional marketing audiences and reporting datasets.
What this demonstrates
SQL and PL/SQL engineering, analytics, product ownership, enterprise-data expertise, production support, and translating business requirements into maintainable data-system behavior.
Engineering Highlights
- Served as both product owner and hands-on developer
- Built and maintained primarily with Oracle PL/SQL and Python
- Operated on complex Ellucian Banner relational data and institutional business rules
- Supported marketing analytics, audience segmentation, and list-generation workflows
- Improved reliability, maintainability, data quality, and performance
- Required SQL optimization, integration, validation, troubleshooting, and production support
- Oracle PL/SQL
- Python
- Ellucian Banner
- SQL
- ETL
- Analytics
- Product Ownership
The Common Thread
The technology changes. The engineering approach does not.
Northstar, ShimStack, Nessie, and Centralist solve very different problems, but they reflect the same way I tend to approach engineering: understand the domain before designing the system, identify where the real complexity lives, and make that complexity manageable through clear models and repeatable processes.
I care about correctness, traceability, maintainability, and explainability because most difficult technical problems do not stay confined to one query, service, application, integration, or team.
That perspective comes from working across both sides of enterprise technology: years of understanding business processes and users, followed by progressively deeper work with the data, analytics, software, integrations, and infrastructure behind them.