Industry
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.
I designed and deployed production ingestion services bringing together heterogeneous data from APIs, databases, energy assets, EV charging systems, solar monitoring, and business platforms.
The same framework supported both operational energy data and external business sources without requiring a dedicated implementation for every new integration.
I implemented reusable patterns for incremental loading, change handling, schema evolution, deduplication, retries, and validation.
This made new sources easier to integrate while keeping ingestion behavior consistent across different datasets.
The services were designed for scheduled production workloads with operational monitoring around execution, freshness, failures, and data quality.
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.
I led the redesign and migration of a reporting environment supporting a broad range of business, operational, engineering, and selected customer-facing analytics.
The work included dashboard migration, reporting standards, reusable templates, stakeholder requirements, and coordination across technical and business users.
Reporting was organized so that users could move from high-level views to progressively more detailed operational and engineering analysis without duplicating the same underlying metrics across separate dashboards.
Shared data models and reporting conventions made it easier for teams to explore data consistently while reducing duplicated dashboards and isolated calculation logic.
The reporting layer covered use cases across renewable-energy assets, EV charging, project execution, finance, operations, forecasting, and reliability.
End-to-end production platform using machine learning for site-specific solar-generation and facility-load forecasts, refreshed every 15 minutes over a three-day horizon and integrated with downstream energy-management workflows.
Spatiotemporal forecasting of regional photovoltaic production from Meteosat image sequences using a ConvLSTM model trained and evaluated against measured production and a persistence baseline.
Scientific framework for evaluating solar forecasts beyond average error metrics, using Temporal Distortion Mix and a ramp metric to assess timing errors and rapid irradiance variations.
GCP