Ex-Accenture consultant leveraging 25+ years of working with data.
Over the years, I've learned that the best developers invest an extraordinary amount of time gaining a deep understanding of requirements --- both business and technical. This deep understanding greatly increases the odds of success of any analytics initiative.
My favourite metaphor is that analytics is really about building tents, not pyramids. An iterative approach ensures that your analytics challenges are tackled in manageable-sized chunks, with each iteration delivering something of value to the business. The iterative approach also allows architectural approaches to be refined over time and lessons learned rapidly.
To quote Sun Tzu -- “Opportunities multiply as they are seized.” This is especially true with delivering analytics initiatives. Staying intensely focused over time on delivering small, incremental analytics capabilities is the best way to deliver break-through insights and opportunities.
Data Engineering. Microsoft Fabric. MongoDB.
B. Engineering, MBA
Adjunct Professor (2020-23)
Data Analytics
University of British Columbia
Working with TEEMA Managed Services, architected and implemented the operational analytics platform for a multi-tenant, FHIR-based clinical registry system.
Tech: Microsoft Fabric, Azure.
Architected and implemented a Lakehouse including ingestion of multiple data sources (Bullhorn, HR Cloud) via API.
Tech: Microsoft Fabric, Azure
Designed and implemented a variety of data integration processes from multiple APIs (Momentus/Ungerboeck, DayForce).
Tech: AWS, Node.
Developed a cloud-based data warehouse that provided a variety of operational and financial reports and dashboards.
Tech: AWS, MongoDB, Node, Momentus/Ungerboeck.
Developed a data warehouse populated from data ingested from DayForce (Scheduling / HR).
Tech: Azure, MongoDB, Node.
Various business and analytics consulting projects over a 10 year period.
Tech: Business Objects, Oracle, Cognos.
