Data Engineer focused on building reliable data platforms, real-time pipelines, and AI-enabled data systems.
- Real-time data processing with Kafka and Spark
- Data orchestration with Airflow
- Analytics engineering with dbt
- AI systems for DataOps and engineering workflows
A production-minded data platform built with Kafka, Spark Structured Streaming, PostgreSQL, dbt, and Airflow.
Key areas:
- event-driven architecture
- Bronze / Silver / Gold data layers
- streaming deduplication and watermarking
- idempotent processing
- data quality and quarantine pipelines
An AI-assisted system for investigating data incidents and supporting DataOps workflows.
Python • SQL • PostgreSQL • Kafka • Spark • Airflow • dbt • Docker
- Spark Structured Streaming
- Airflow orchestration
- distributed data reliability patterns