Capability 02 / Data engineering
Plenty of teams can stand up a pipeline. Far fewer build one that survives a schema change, a bad batch at 3am, and the departure of the person who wrote it. We build the second kind — and leave your engineers able to change it without fear.
The shape of it
Medallion is not a slogan for us. Each tier has a job, a contract, and a test that proves it is doing it. Data moves up the tiers only when it has earned the right to.
Bronze
Raw, append-only, exactly as the source produced it. Schema evolution handled, nothing silently dropped, full replay possible when you inevitably need to reprocess history.
Silver
Conformed, deduplicated, history tracked properly with SCD where the business actually needs it. This is where data-quality expectations are enforced, not hoped for.
Gold
Business-facing marts and metrics with agreed definitions, plus the single governed surface that BI and AI both read from. One version of the number, everywhere.
What we build
Auto Loader for files, Structured Streaming for events, log-based CDC for databases — all landing on Delta with idempotency and replay built in from the first commit.
Data quality expressed as code, checked on every run, with a policy for what happens when it fails — quarantine, halt, or alert. Bad data does not get to be somebody's problem later.
Catalogs, schemas, groups and grants designed deliberately, expressed as Terraform, with lineage and audit turned on. Access is a reviewable artifact, not tribal knowledge.
Asset Bundles, environment promotion, and Lakeflow orchestration, plus the cost guardrails and cluster policies that stop the bill from surprising anyone at the end of the month.
# Bad data stops here, loudly, not three tables downstream.
from quasar.dq import expect, on_fail
@expect(
"order_id is not null",
"amount >= 0",
"order_ts <= current_timestamp()",
policy = on_fail.QUARANTINE,
)
def orders_silver(bronze):
return (
bronze
.dropDuplicates(["order_id"])
.withWatermark("order_ts", "2 hours")
)
# 3 expectations - 0.02% quarantined - lineage publishedHow we build
The difference between a pipeline that lasts and one that rots is almost never the clever part. It is the discipline around it. We are deliberately opinionated about that discipline.
Straight answers
No. We start from what unblocks the business and extend outward. Plenty of existing pipelines are fine and stay as they are. We are not interested in rewriting working code for its own sake.
We prefer it. Pairing is how the platform stops being ours and starts being yours. The goal of every engagement is a team that no longer needs us, and that only happens if they were in the repo the whole time.
Cost is a design input, not an afterthought. Cluster policies, serverless where it fits, and guardrails that make the bill predictable are part of the build, and we show you the model before we commit to it.
Start here
Bring your current architecture, data volumes and the workloads that hurt. You leave with a target-state diagram on Databricks, the three changes that would cut the most cost or latency, and an honest answer on whether you need us at all.
No slide deck. No procurement process. One senior architect on the call.