The original multi-client reporting system, which I developed using PowerShell and Tableau's TabCmd utility, automated only the report download stage. While it offered some efficiency over fully manual processes, the solution lacked end-to-end automation and presented challenges for cloud migration, as PowerShell was tightly coupled to on-prem infrastructure.
To address these limitations and improve the system’s efficiency, scalability, and cloud readiness, I was tasked with reengineering the entire dashboard delivery pipeline. Rather than applying a quick fix, I conducted a full process review and applied systems thinking to streamline operations end-to-end. Working closely with analysts, we created standardized dashboard templates to serve most clients, while building flexible, custom solutions for exceptions.
I then engineered a new reporting pipeline using Python and the Tableau API, a platform-independent approach more suited to cloud environments. The new system connected data sources directly to dashboard outputs and included built-in monitoring and error-handling for robust, automated delivery.
This transformation turned a labour-intensive, partially automated process into a scalable, cloud-ready, data-driven reporting solution. It reduced analyst workload, improved delivery reliability, and enhanced system transparency through proactive monitoring and alerting.
Another client reporting system was built on a complex OLAP cube.Over time, the cube architecture became a significant bottleneck, causing slow refresh cycles, high maintenance costs, and delays in delivering client insights.
I was tasked with decommissioning the legacy OLAP cube and redesigning the ETL process to enable a faster, more scalable reporting infrastructure that could support business needs with greater agility.
Working collaboratively with a data engineer, we reengineered the ETL pipeline to directly transform and load data into a new set of flattened, business-optimized tables. we prioritized core reporting requirements to streamline schema design while modularizing a way to use additlion dimensions if needed to maintain flexibility. This shift eliminated the dependency on complex cube processing and allowed reporting tools like Tableau to query data more efficiently.
The decommissioning of the cube and implementation of the new ETL and table structures significantly reduced data refresh times, cut maintenance efforts by approximately 30%, and enhanced reporting performance. As a result, operational efficiency improved and the technical debt was reduced.