Cloud data platforms · Lakehouse architecture · Production ETL pipelines · Data quality & observability
I design and build cloud data platforms that turn fragmented sources into reliable, observable, analytics-ready systems. My work spans data engineering, data architecture, and platform automation — incremental ETL pipelines, medallion lakehouse design, contract-driven data quality, and production operations on AWS, Azure, Databricks, and Snowflake.
Design for failure.
Automate repeatable work.
Measure data quality.
Document decisions.
Keep systems understandable.
| Domain | Technologies & practices |
|---|---|
| Data engineering | Python · SQL · incremental ingestion · ETL/ELT · API pipelines · checkpointing |
| Data architecture | Medallion lakehouse · bronze/silver/gold layers · lineage · governance · cost modeling |
| Orchestration | Apache Airflow · dbt · Prefect · Spark |
| Cloud platforms | AWS · Azure · Databricks · Snowflake |
| Quality & observability | Data contracts · schema validation · freshness checks · alerting · CI/CD |
Production-style open-source projects with architecture docs, tests, CI, and operations runbooks.
| Project | What it demonstrates |
|---|---|
| production-data-pipeline | Incremental API ingestion · PostgreSQL bronze layer · dbt silver/gold models · Airflow orchestration · webhook alerts · v0.1.0 release |
| data-quality-observability | YAML data contracts · schema/null/uniqueness/freshness checks · check history · alert routing · Airflow scheduling |
| cloud-lakehouse-blueprint | Medallion architecture manifests · Terraform IaC · IAM governance · lineage graph · validate --json for CI |
Contributions to widely used data-platform projects:
| Project | PR | Change |
|---|---|---|
| Prefect | #22500 ✓ merged | Document /api/health vs /api/ready for Kubernetes probes |
| Apache Airflow | #69857 | Update SQLAlchemy pooling link to 2.0 docs |
| dbt docs | #9606 | Troubleshooting note for prefixed custom schema names |
- production-data-pipeline — structured ingestion summary metrics, webhook alerts for zero-record runs and Airflow failures, local
scripts/check.ps1 - data-quality-observability — customers data contract,
dqo_contract_checksAirflow DAG (orders + customers) - cloud-lakehouse-blueprint —
validate --jsonfor CI pipelines, deployment docs, changelog - Prefect — merged upstream docs PR on Kubernetes health vs readiness probes
I publish focused, production-style data platform projects — not toy demos. Each repo includes architecture decision records, pytest coverage, GitHub Actions CI, and honest iteration through releases.
Open to:
- Open-source data platform and data engineering contributions
- Data architecture reviews and lakehouse design discussions
- Data quality, observability, and pipeline automation tooling
- Technical collaboration with engineering teams and OSS maintainers
Website: br413.github.io · Portfolio: production-data-pipeline · Release: v0.1.0 · Lakehouse blueprint: cloud-lakehouse-blueprint · Data quality: data-quality-observability
Focus areas: data engineering · data architecture · cloud data platforms · lakehouse · ETL pipelines · dbt · Airflow · data quality · observability · Terraform · AWS · Azure · Databricks · Snowflake