dbt structures SQL transformations within an analytics platform as version-controlled, tested and documented models. It helps organize business logic into reusable layers, control changes and provide consistent data to reporting and analytics tools.
Data orchestration and transformation pipelines to deliver high-quality, actionable data
I led the modernization of a previously fragmented transformation layer into a structured analytics-engineering workflow.
Data transformations were reorganized into reusable layers separating shared data preparation, analytical models, and reporting outputs.
I introduced version control, automated testing, documentation, lineage, code review, and continuous integration practices around data transformations.
These standards made transformation logic easier to review, maintain, and extend across different analytics use cases.
I standardized reusable data models and business definitions so that reporting and analytical applications could rely on consistent transformation logic rather than duplicating calculations independently.
Designed the analytics model around individual charging ports, with derived reliability intervals that could be aggregated consistently from charger level through site and portfolio reporting.
Input data were normalized onto a consistent time basis before aggregation, simplifying the calculation of time-weighted availability across heterogeneous equipment.
Implemented automated validation around asset coverage, reporting periods, downtime exclusions and metric consistency.
The resulting transformation pipeline provided a reproducible definition of uptime rather than relying on dashboard-level calculations or manually maintained metrics.
The models were implemented as production analytics workflows and maintained through version-controlled transformations, automated testing and documented metric definitions.
End-to-end development and modernization of a data platform spanning production ingestion, analytics engineering, quality controls, and reporting for operational, business, and customer-facing use cases.
Reliability analytics for 70,000+ managed EV chargers, reconstructing consistent per-port uptime from operational state, connectivity, asset history, and downtime exclusions for operational and customer-facing reporting.
