Snowflake is a managed cloud data platform that separates data storage from analytical compute. It provides a scalable foundation for centralizing heterogeneous data, running SQL transformations and serving governed datasets to analytics, reporting and data applications.
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.
Built a consistent reliability dataset from charger operational states and connectivity information collected across a heterogeneous EV charging network.
For each charging port, operational and online status were combined on a common time basis so that availability could be evaluated consistently across equipment and sites.
Reporting logic also accounted for changes in the charger fleet over time, including commissioning periods, decommissioned or replaced equipment, site reporting windows and asset reassignment.
This ensured that reliability calculations were based on the equipment that was actually expected to be operational during each reporting period.
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.
