br413

Senior Data Engineer

Cloud data platforms · Reliable pipelines · Developer automation

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 · Prefect · Spark
Cloud	AWS · Azure
Quality	Data testing · CI/CD · Monitoring · Lineage · Governance

Project	Highlights
production-data-pipeline	Incremental ingestion · ingestion metrics · webhook alerts · dbt · Airflow · v0.1.0
data-quality-observability	Orders & customers contracts · check history · alert routing · Airflow DAG
cloud-lakehouse-blueprint	Medallion manifests · Terraform IaC · `validate --json` · lineage · cost modeling

Project	PR	Change
Prefect	#22500 ✓	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_checks Airflow DAG (orders + customers)
cloud-lakehouse-blueprint — validate --json for CI pipelines, deployment docs, changelog
Prefect — merged upstream docs PR on Kubernetes health vs readiness probes

I publish focused, production-style projects with architecture docs, tests, CI, operations runbooks, and honest iteration — not just code samples.

Open to:

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