DuckDB is an in-process analytical database designed for fast SQL analysis without a separate database server. It is useful for local data exploration, pipeline development and validation, particularly when working directly with tabular files or Python data workflows.
Data orchestration and transformation pipelines to deliver high-quality, actionable data
Design and development of reliable, scalable software systems
Extracting insights and building predictive models from data using statistics, machine learning, and programming
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.
