What this is
Building and repairing ETL/ELT pipelines, standardizing transformations, and adding tests + observability.
Who it’s for
- SMEs scaling from spreadsheets/manual exports
- Startups hitting “data breaks every week”
- Teams adopting dbt/Airflow/warehouse best practices
- Agencies needing dependable client reporting pipelines
Typical engagements
Pipeline & Data Quality Audit (1–2 weeks)
- Map sources → transformations → outputs
- Identify failure points, costs, bottlenecks
- Propose prioritized plan
Build/Refactor ELT Stack (3–10 weeks)
- dbt project structure (staging/marts)
- Airflow orchestration + backfills
- Tests + documentation + lineage
- Alerting (Slack/email)
Operate & Improve (monthly)
- Monitor failures, improve reliability
- Optimize cost/performance
- Add new sources and marts
Deliverables
- Repo: dbt models + orchestration + tests
- Monitoring/alerting configured + documented
- Runbooks (“what to do when X fails”)
- Handover workshop
For technical readers
- dbt model design (staging → intermediate → marts)
- Incremental models, snapshots, SCD patterns
- Airflow DAG patterns, backfills, idempotency
- Automated tests (dbt tests, custom checks)
- Monitoring: freshness, anomaly detection, SLA alerts
- Documentation: lineage, source freshness, runbooks
Why I’m good at this
- Built robust ETL pipelines using dbt + Airflow with automated tests and quality checks
- Experience with modern warehouses and operational monitoring
- Comfortable bridging business needs with durable engineering patterns
How I work
- Start with a small “vertical slice” pipeline end-to-end
- Add tests + monitoring early (not as an afterthought)
- Document ownership and definitions continuously