Data Platform Engineer · SF Bay Area
I take data platforms from proof of concept to production.
Right now that's a data lakehouse on Kubernetes: one query engine over 30+ data sources that powers a customer-facing product and about a dozen internal teams. Plus a Kafka streaming platform, AI tooling, and the on-call and disaster-recovery that keep it running.
Across the data lifecycle
The platform I run, by the numbers
What I do
I work across the stack that makes analytics possible: storage, query engines, streaming, identity, and governance.
A single place to query the company's data wherever it lives, on-prem or in the cloud, without copying it somewhere first. I run it on Dremio and an Apache Iceberg catalog: one query engine over more than 30 data sources. It backs a customer-facing product and the internal teams that build on it.
Keeping a service people depend on running and trustworthy: automated deploys, on-call and incident response, a disaster-recovery plan, and the dashboards that catch trouble early.
Moving data the moment it's created instead of waiting for a nightly batch, with a Kafka streaming platform whose events land straight in the lakehouse. On top, AI tooling that documents the data catalog and answers questions about it from your code editor.
Selected work
Platform
A production-critical service at 99.9% uptime. It powers a customer-facing SaaS product and is queried directly by a dozen internal teams, data scientists, and engineers.
Streaming
Self-hosted, secured real-time data streaming, running in staging. The streamed data lands straight in the lakehouse, ready to query.
AI / Platform
Writes documentation for a data catalog every day, and answers questions about it from an engineer's code editor.
Writing
Happy to talk data platforms, lakehouses, or where AI actually earns its keep in infrastructure. LinkedIn is the fastest way to reach me.