teamster 🚛
Next-gen data orchestration for KIPP TEAM & Family Schools
Teamster is the data engineering platform powering analytics and reporting
across KIPP Newark, Camden, Miami, and Paterson. It ingests data from 30+ source
systems, transforms it through dbt, and delivers it to Tableau, Google Sheets,
PowerSchool, and other consumers — all orchestrated by Dagster.
- 🎻 Dagster — orchestrates every ETL step across five code locations, one
per school network;
observe and run pipelines in Dagster Cloud
- 🔧 dbt — transforms raw source data into staging, intermediate, mart, and
extract models in Google BigQuery
- 🚿 dlt — loads data from API sources into BigQuery alongside dbt
- 🔀 Airbyte — managed connector pipelines for select integrations
- 🪣 Google Cloud Storage — intermediate storage layer between pipeline
steps
- ☸️ Google Kubernetes Engine — runs each code location in its own container
in production
- ⚙️ GitHub Actions — CI/CD for building and deploying code locations
- 📊 Tableau — primary BI consumer; Dagster manages workbook extract
refreshes
📖 Background
KIPP's data infrastructure was previously a patchwork of Python scripts, cron
jobs, stored procedures, Fivetran, and Selenium automation spread across
multiple databases. Synchronous scheduling meant a slow pull from one system
would cascade into downstream failures. A single data engineer spent more time
firefighting than building.
Teamster replaced all of it with a unified, asset-based platform. The results:
- ⚡ Pipeline development time dropped from weeks to days
- 🎫 Data-related support tickets fell 30% year-over-year
- 🧑💻 Analysts gained Git, SQL, and DevOps skills through shared PR workflows
- 🔔 Real-time Slack alerts replaced reactive debugging
"The visibility into the pipelines is a game changer. We know as soon as
something fails and why."
Read the full story in the
Dagster case study.
🚀 Get started
New to the project? Start here:
- Guides — account setup and task-focused walkthroughs
- Architecture — how the code is organized
- Contributing — workflow and PR guidelines
📚 Reference
🗺️ Guides & Troubleshooting
