// work & skills
What I do
Cloud Data Engineering on Google Cloud Platform — building and operating the systems that move, transform, and make sense of data at scale.
This is my personal site and does not represent my employer or any organization I work for.
Google Cloud Platform
The stack I live in
These are the GCP services I work with regularly — not a checkbox list, but tools I've used to solve real problems.
BigQuery
Data Warehouse
Dataflow
Stream & Batch Processing
Pub/Sub
Messaging & Streaming
Cloud Composer
Workflow Orchestration
Cloud Storage
Object Storage
Dataproc
Spark & Hadoop
Cloud Functions
Serverless Compute
Cloud Run
Containerized Workloads
Looker / Looker Studio
BI & Visualization
Vertex AI
ML Platform
IAM & Security
Access & Identity
Terraform (GCP)
Infrastructure as Code
Skills
Across the stack
Data Engineering
Languages & Tools
Practices
Approach
How I work
Reliability over speed
A pipeline that processes data correctly 100% of the time is more valuable than one that processes it twice as fast with silent failures. I build observability in from the start, not as an afterthought.
Cost-conscious by default
Cloud costs compound quickly without intent. I think about query efficiency, partition strategies, and data lifecycle from the design phase — not after someone gets a billing alert.
Infrastructure as code
If it can't be reviewed, version-controlled, and reproduced, it's a liability. Terraform for infrastructure, dbt for transformations, Airflow for orchestration — all of it in source control.
Documentation that lasts
I write documentation for the next person who has to understand the system at 2am when something breaks. The goal is clarity, not coverage. One sentence that explains the why beats five that describe the what.
Next
See what I've built
Personal projects and open work, including The Prompt Kitchen.