Scalable Data Platforms for Growing Enterprises
Nearly a decade of experience architecting, migrating, and automating enterprise data on Microsoft Azure and Google Cloud Platform for growth-stage and mid-market companies.
Book a Platform ReviewHow I Help Teams & Businesses Scale
Legacy to Cloud Migrations
Specializing in moving on-premise Postgres and SQL Server data platforms to the cloud, modernizing infrastructure without disrupting business operations.
Workflow Automation
Freeing up your analysts by replacing manual Excel and other file operations with automated pipelines using modern orchestration tools and serverless functions.
Lakehouse & Modern Data Warehouse Architecture
Consolidating fragmented data silos into unified, high-performance platforms using Azure Synapse Analytics, Azure Databricks, and Microsoft Fabric.
Recent projects:
Modernizing Legacy Infrastructure
The Problem: Brittle, on-premise SSIS packages took 90 minutes to run and frequently failed, requiring manual babysitting. Worse, analysts couldn't integrate external API data, leaving leadership to make decisions based on fragmented, incomplete dashboards.
The Impact: Slashed daily pipeline runtimes down to 30 minutes and engineered atomic error handling to eliminate full system reruns. Seamless API integration finally provided the C-Suite with a holistic, 360-degree view of business performance.
The Tech: Azure Data Factory, Azure SQL, REST APIs.
Rescuing the Analyst Team
The Problem: Highly paid analysts were wasting 20+ hours a week manually wrangling Excel reports instead of generating insights.
My Impact: Recovered over 1,000 hours annually, allowing the team to shift focus to predictive analytics and strategic forecasting.
The Tech: Fully automated lifecycle via Azure Functions and LogicApps.
Resolving Conflicting Business Metrics
The Problem: A fragmented data environment spread across poorly integrated tools meant that critical sales and fulfillment metrics rarely matched across silos. This lack of governance also bottlenecked the business from quickly leveraging new API data sources.
The Impact: Architected a unified platform that established a definitive "single source of truth" for enterprise analytics, completely eliminating metric discrepancies. The centralized architecture also cut the time required to integrate new data sources by 50%.
The Tech: Azure Synapse Analytics, Airbyte.
R&D: Real-Time Geospatial Data Product
The Initiative: To stay ahead of the technology curve, I architected an independent proof-of-concept to evaluate modern open-source lakehouse capabilities and real-time data federation.
The Impact: Engineered a low-latency data product that seamlessly ingests messy, disparate data streams (government and social APIs) into an interactive recommendation engine. This demonstrates the ability to rapidly prototype, test, and deploy highly scalable, cost-effective data architectures.
The Tech: Apache Iceberg, Trino, dbt, MinIO, Streamlit.
Remediating AI-Generated Technical Debt
The Problem: A client deployed an AI-coded proof of concept to move fast, but subtle inefficiencies in the generated code overwhelmed their Azure SQL operational data store. This resource drain severely delayed critical production reporting and threatened core business operations.
The Impact: Acted as a specialized remediation expert to untangle the AI-generated code. Rewrote sub-optimal queries, fixed connection pooling bottlenecks, and strategically segmented compute resources. This immediately restored reporting SLAs and allowed the business to safely scale their AI initiatives without risking production stability.
The Tech: Azure SQL, Performance Tuning, Resource Workload Management.
About Me
I build robust, scalable data systems that help companies make better decisions, faster. Originally cutting my teeth in the Australian tech market, I relocated to Denver, Colorado in 2023, bringing a portfolio of international clients with me.
After getting my start as a SQL Server DBA, I have spent the last decade working within Microsoft Azure and Google Cloud data platforms. From writing transformations with serverless functions to designing and implementing Databricks Lakehouses, I know the nuances, the pitfalls, and the most cost-effective ways to scale in the Azure and GCP ecosystems.
Ready to get your data stack on track?
Let's grab a coffee in Denver or jump on a quick call to discuss your data engineering bottlenecks.
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