Data engineering and architecture
I build data platforms from nothing, then make sure someone other than me can run them.
I'm a senior data engineer with 9+ years of experience, focused on Snowflake, dbt, and AWS. My specialty is greenfield work: taking a platform from concept to production, and designing event-driven ingestion architecture that holds up once real, messy upstream systems start sending it data.
I'm looking for Data Architect, Lead, or Staff Data Engineer roles where the job is owning a platform end to end, architecture, build, operations, and the judgment calls about what to harden and when, not maintaining someone else's system.
I use Claude Code daily as a normal part of how I build, the same way I'd use a linter or a terminal. It's part of why I can take on an unfamiliar tool or language without treating that as a blocker.
Case studies
Every project below is real work. Company names are left out and details are described generically by industry to avoid any confidentiality issue. Numbers are exact.
- 01Greenfield Snowflake, dbt, and AWS Data PlatformBuilt a production data platform from concept to functional production in about 2.5 months, covering 8+ business domains across 5 environments.Snowflake · dbt · AWS EventBridge · S3 · Lambda · Terraform · Docker2.5 moconcept to production
- 02Multi-Tenant Access ScopingBuilt tenant-scoped access patterns for a multi-tenant reporting service. Foundational work, and I'll say plainly where it stops.Snowflake · SQL · dbtFoundationalquery-layer scoping
- 03Legacy Modernization and MigrationMoved critical workloads off Oracle, SSIS, and MS Access onto Snowflake and automated ETL, cutting an hour-long process to about ten minutes and eliminating 250+ hours of manual work a year.Snowflake · Oracle · SSIS · Python · MSSQL · DB2 · SQL~1hr → ~10minOracle workload runtime
- 04Production ML Platform SupportKept production ML scoring models and their upstream data pipelines running, including a live TensorFlow 2.13 to 2.15 upgrade with no disruption to inference.TensorFlow · Python · Snowflake · Airflow2.13 → 2.15TensorFlow, live upgrade
Platform maturity assessments
Building something and being accountable for whether it's actually production-grade are different skills. I've run maturity assessments across architecture, governance, security, and operational practice on a production platform, identifying gaps nobody had flagged yet and turning that into a prioritized remediation roadmap, rather than a list of vague concerns.
Stack
| Snowflake | Streams, Tasks, Stored Procedures, Snowpipe, Warehouse management, Query optimization |
|---|---|
| Transformation | dbt |
| AWS | EventBridge, S3, Lambda, IAM, CloudWatch |
| Infrastructure | Terraform, Docker, Airflow, Control-M |
| Languages | Python, SQL |
| CI/CD | Git/GitHub, GitHub Actions, Jenkins, Harness |