North America, remote-first
Based in Vancouver, working with startups and scale-ups across the US and Canada.
Pipelines Version-controlled. Tested. Production-grade. before Models Which only work if the data underneath them does.
Anyone can recommend an architecture. I'm the one who builds it, tests it, and stays on the hook once it's running in production.
About meHow I work
One senior engineer, every engagement
No account managers, no junior bench, no handoff mid-project.
7+ years building data infrastructure at Big Tech
CI/CD, infrastructure-as-code, and automated testing: standard practice there, applied here.
Built for teams like these
- Seed–Series A startups
- VC/PE-backed scale-ups
- Mid-market SaaS & FinTech
- Teams shipping RAG & GenAI
- Fragmented spreadsheet-and-script stacks
- Companies without a data lead
- Cloud cost overruns
- Compliance-exposed pipelines (GDPR, HIPAA, SOC 2)
Tools I work in
- dbt
- Snowflake
- Databricks
- Airflow
- Terraform
- Fivetran
- Great Expectations
- GitHub Actions
- BigQuery
- Redshift
How I can help
Three ways to fix the foundation
Every engagement starts the same way: a data & AI-readiness audit that shows exactly where the pipeline breaks before I scope anything bigger.
AI-Ready Data Foundations
Semantic layers, metadata, and clean lineage so RAG and AI systems retrieve the right data instead of hallucinating.
Production DataOps & Pipeline Engineering
Version-controlled, tested, CI/CD-deployed pipelines that replace fragile scripts and catch schema drift before it reaches a dashboard.
Fractional Data Leadership
Senior data engineering judgment on retainer, for teams that need it but can't yet justify a full-time hire.
Every engagement starts with a data & AI-readiness audit.