I design and build data systems that turn fragmented sources into reliable, observable, analytics-ready platforms.
Design for failure.
Automate repeatable work.
Measure data quality.
Document decisions.
Keep systems understandable.
| Area | Stack |
|---|---|
| Languages | Python · SQL |
| Platforms | Databricks · Snowflake · Lakehouse architectures |
| Orchestration | Airflow · dbt · Spark |
| Cloud | AWS · Azure |
| Quality | Data testing · CI/CD · Monitoring · Lineage · Governance |
| Project | Highlights |
|---|---|
| production-data-pipeline | Incremental ingestion · PostgreSQL bronze · dbt silver/gold · Airflow DAG · v0.1.0 |
Open-source contributions
- apache/airflow#69857 — SQLAlchemy 2.0 documentation link
- dbt-labs/docs.getdbt.com#9606 — prefixed schema troubleshooting
I publish focused, production-style projects that demonstrate architecture, testing, documentation, deployment and operational reliability — not only code samples.
Recent milestone: v0.1.0 with ingestion, dbt transforms, Airflow orchestration, operations runbook, and green CI.
Open to:
- Open-source data-platform contributions
- Backend, cloud and automation projects
- Data-quality and observability tooling
- Technical collaboration with engineering teams and maintainers
Portfolio: https://github.com/br413/production-data-pipeline · Release: https://github.com/br413/production-data-pipeline/releases/tag/v0.1.0
Primary interests: data platforms · distributed systems · cloud engineering · developer tooling · applied AI