About

Data & Platform Engineer focused on building robust data infrastructure and aggressively optimizing system costs. I engineer the right architecture for the job, balancing scalable cloud deployments with highly efficient, cost-effective on-premise bare-metal solutions.

Business Impact

Infrastructure Savings: Slashed data processing and infrastructure costs by 60% by strategically provisioning GCP Preemptible and Spot VMs.
Operational Efficiency: Reduced operational overhead and risks by 60% by enforcing strict Infrastructure as Code (IaC) governance using Terraform and Ansible.
On-Premise Optimization: Built a cost-effective on-premise data lakehouse from scratch on Kubernetes (Spark, Airflow, DuckDB, MinIO) to strictly control development and analytics spend.
Deployment Velocity: Cut pipeline development and deployment lifecycles by 60% by migrating monolithic Windows-based platforms to lightweight, highly-available K3s clusters.

Core Tech

Infrastructure & OS: Kubernetes (K3s), Docker | Linux (Immutable: OpenSuse MicroOS, Fedora Silverblue; Mutable: Red Hat, Ubuntu).
Data & Orchestration: Apache Airflow, Prefect, Spark, DuckDB, MinIO | BigQuery.
Automation: Terraform, Ansible, GitLab CI.
Languages: Python, SQL, C, Bash, Clojure.

Let's Connect

I'm always open to discussing new projects or sharing ideas.

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Last Updated: March 13, 2026, 8:45 a.m.