Data Engineer focused on scalable pipelines, cloud infrastructure, and reliable analytics delivery.
I design and ship production-style data platforms — both batch and streaming — on AWS, with Python, SQL, Airflow, Kafka, and Spark. From raw ingestion to curated marts, I care deeply about modular ETL, orchestration, data quality, and ops-ready deployments (Docker, CI/CD, observability).
focus: [ELT, Streaming, Cloud Data Platforms]
stack: [Python, SQL, AWS, Airflow, Kafka, Spark, dbt, Docker]
|
|
|
|
|
|
|
|
flowchart LR
A[Sources<br/>APIs · Files · DBs · Streams] -->|Ingest| B[Raw / Bronze<br/>S3 · Kafka]
B -->|Clean & Conform| C[Staged / Silver<br/>Spark · dbt]
C -->|Model & Curate| D[Marts / Gold<br/>Snowflake · Redshift]
D --> E[Consumers<br/>BI · ML · Apps]
F[Airflow] -.orchestrates.-> B
F -.orchestrates.-> C
F -.orchestrates.-> D
G[Observability<br/>Prometheus · Grafana · Logs] -.monitors.-> B
G -.monitors.-> C
G -.monitors.-> D
style A fill:#1f6feb,stroke:#58a6ff,color:#fff
style B fill:#cd7f32,stroke:#58a6ff,color:#fff
style C fill:#c0c0c0,stroke:#58a6ff,color:#000
style D fill:#ffd700,stroke:#58a6ff,color:#000
style E fill:#238636,stroke:#58a6ff,color:#fff
style F fill:#017CEE,stroke:#58a6ff,color:#fff
style G fill:#E6522C,stroke:#58a6ff,color:#fff
310+ contributions last year · actively shipping pipeline & platform work
Open to Data Engineering, Streaming, and Cloud Pipeline opportunities.