I'm on a data platform engineering team, working with Airflow, Kafka, Snowflake, and the usual suspects around data pipelines and warehouse infrastructure. Before this I spent years in distributed systems, platform engineering, and architecture, building developer platforms from scratch and running production at scale. AWS Certified Solutions Architect.

I'm also an AI advocate at work, focused on finding practical applications for LLMs and agentic workflows across the engineering org. I go deep when needed (kernel parameters, GPU passthrough, audio latency) and wide when the architecture calls for it.

I run NixOS on all my machines. My home network doubles as a lab for container orchestration and local AI experiments on consumer GPUs.

Career

2020–now
Staff / lead engineer, data platform
Data pipelines, developer platforms, infrastructure, AI advocacy.
2010s
Infrastructure & platform engineer
AWS, Terraform, containers, GraphQL across production environments.
2007
Started writing code
PHP, shell scripts, system administration.

Skills & tools

Languages

GoPythonTypeScript SQLRubyShell

Data platform

AirflowKafkaSnowflake dbtSpark

Infrastructure

AWSTerraformKubernetes GraphQLgRPCObservability CI/CDDistributed Systems

AI & ML

LLM IntegrationsAgentic Workflows Local InferenceRAG GPU Compute

Systems

Linux/NixOSNetworking PerformanceSecurity
Some books I return to:
  • Design Patterns Gamma et al.
  • SRE Google
  • Domain-Driven Design Evans
  • SICP Abelson & Sussman
  • UNIX: A History Kernighan
  • Microservices Patterns Richardson

Working on data platforms, AI tooling, or infrastructure?
I'd be glad to compare notes.