● live data infrastructure, built to run unattended
End-to-end data engineering CI/CD pipelines — ingestion, migration, warehousing, and analytics — delivered as versioned, tested, observable pipelines. Not a one-off script. A system someone else can maintain after we leave.
Pipelines running in production on
stages
Every engagement runs through the same disciplined sequence — because skipping a stage is where data platforms quietly start to fail.
We design and build the pipelines that move data from wherever it's born to wherever it needs to work: batch ETL/ELT, streaming ingestion, change-data-capture, and event pipelines. Built in Python, orchestrated, retried, and alerting before they ever touch production.
Legacy database to cloud warehouse. On-prem to lakehouse. One vendor to another. We map schemas, reconcile every row, and cut over with a rollback plan — so migration day is uneventful, which is exactly the goal.
Modern warehouse architecture on Snowflake, BigQuery, or Redshift — dimensional models or wide tables, whichever your access patterns actually need. Layered raw → staged → curated, with dbt managing every transformation as version-controlled SQL.
Curated data is only useful if someone trusts it enough to decide with it. We build the metrics layer, define the business logic once, and hand off models that answer real questions — not just dashboards that look busy.
Dashboards built for the decision someone actually has to make, not for a screenshot. We design the view, wire it to the warehouse, and ship it in Looker, Power BI, Tableau, or a custom Python app when the off-the-shelf tool can't keep up.
Every pipeline above is built the same way: Python codebase, Git version control, peer-reviewed pull requests, and CI/CD that tests and deploys changes automatically. Infrastructure as code. No untracked changes in production, ever.
stack
No logo soup. This is roughly how a request actually moves through a system we'd build for you.
workflow
The same Git → CI/CD discipline software teams take for granted, applied to data pipelines.
engagement
Source systems, data volumes, downstream dependencies, and the actual questions the business needs answered — documented before a single pipeline is written.
Working pipelines in a shared repo from week one. You can watch the commit history, not just wait for a final reveal.
Architecture diagrams, runbooks, and a codebase your engineers can read on day one — no tribal knowledge left in our heads.
Post-launch monitoring and tuning until the pipeline is reliable enough that nobody thinks about it. That's the actual finish line.
● pending awaiting your first commit
A short call is usually enough to tell whether this is a quick fix or a full pipeline rebuild. Either way, you'll leave with a clearer picture than you came in with.