Data Engineer with 5+ years designing and building end-to-end data platforms — from raw ingestion to production-ready dashboards. I work across the full data stack: scalable ETL/ELT pipelines, cloud data warehousing, data modeling, and BI delivery.

I focus on automation over manual work, scalable architectures, and production-ready code. Every system I build is designed to run reliably at scale, with proper governance, environment separation (dev/prod), and observability built in from day one.

[Sources]  →  ingestion  →  transform  →  serving  →  Consumers
              (Python /      (dbt /        (FastAPI /   (BI / APIs)
               Snowpark /     PySpark /     Cloud Run /
               Lambda)        SparkSQL)     Kubernetes)
                  ↕               ↕              ↕
            GCS / S3        BQ / Snowflake  REST APIs
            Raw layer       Staging → Mart  Dashboards

[Events]   →  streaming  →  Delta / BigQuery
              (Kafka +
               PySpark)

[Infra]    →  Terraform  →  GCP · AWS · Snowflake · Databricks

• Data pipelines — production ETL/ELT with retry logic, error handling, structured logging
• Data modeling — dbt projects with staging → intermediate → mart layer pattern
• Medallion architecture — bronze → silver → gold on Databricks with Delta Lake
• Cloud infra — GCP and AWS environments managed with Terraform, dev/prod separation
• Real-time streaming — Kafka + PySpark Structured Streaming with watermarking
• Data serving — FastAPI over BigQuery mart tables, deployed on Cloud Run and Kubernetes
• Orchestration — Airflow DAGs coordinating cross-platform pipelines

Repositories follow {layer}-{platform}-{domain} naming with dev/prod separation, CI/CD via GitHub Actions, and semantic versioning on every release.