Google Cloud provides managed infrastructure for hosting applications, storing data and running analytical or machine-learning workloads. It supports moving a project from development to production with shared services for deployment, scaling, security and operations.
Data orchestration and transformation pipelines to deliver high-quality, actionable data
Design and development of reliable, scalable software systems
Operationalizing machine learning models through automated deployment, monitoring, versioning, and lifecycle management in production environments
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.
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.
