Data engineer with hands-on experience building ELT pipelines and lakehouse-style analytics stacks in regulated environments. I like owning pipelines end-to-end, improving reliability, and delivering measurable outcomes.
Lakehouse architectures, Airflow orchestration and practical data quality.
This ELT project loads source files from object storage, stages them in an analytical database, and then transforms them into standardized, history-aware dimensional tables. Raw data is first copied into a dated backup area, then ingested into staging schemas with minimal structure changes. Next, transformation models build SCD2-style dimensions, applying data quality checks to ensure consistency and traceability. The entire pipeline (backup → load → transform → test → document) runs on Kubernetes cluster and orchestrated by Airflow.
Python CLI that orchestrates three data maintenance modes over S3 and Trino/Iceberg:
backup (copy CSVs between S3 accounts into date-based folders),
load (normalize columns and write into Trino *_staging tables),
and cleanup (delete old backups by retention period). All behavior is
configured via environment variables and CLI arguments.
Simple website deployment automation with GitHub Actions and Terraform.
Automated weather data ingestion to Postgres with subsequent analysis.
Pipelines for ML experiments and price prediction.
Prototype with data preparation and a web UI.
Containerised example with Airflow orchestration.
