Custom ETL and ELT pipeline development for marketing and commerce data: incremental loads, restatement handling, orchestration, and warehouse-native transforms.
The pipeline that works until it matters
Most reporting breaks in the same three places. A full-table reload that took four minutes in year one now takes six hours. A job fails at 2am, retries into a half-written table, and nobody notices until a client asks why revenue dropped. And history quietly changes underneath you, because ad platforms and marketplaces restate conversions for days after the fact. A pipeline that assumes yesterday is final will be wrong every single day.
What we build
- Incremental extract and load pipelines sized to your data volume, not full reloads
- ELT patterns that land raw data first and transform inside the warehouse, so a bad transform never costs you the source
- Restatement windows that re-pull the trailing period ad platforms are still changing
- Orchestration in Airflow or Dagster with dependencies, retries, and idempotent tasks
- dbt transformation layers with tests, so a null or a duplicate fails the run instead of reaching a dashboard
- Backfill tooling your team can run themselves when history needs to be rebuilt
How we work
Trace the current flow source by source and find where numbers actually diverge
Land raw ingestion first so nothing depends on a transform being right on day one
Model and test the reporting layer in dbt, then cut dashboards over channel by channel
Add alerting and hand over runbooks and backfill commands
Typical stack
Frequently asked questions
ETL transforms data before it lands; ELT lands it raw and transforms inside the warehouse. We default to ELT because warehouse compute is cheap, raw history means you can rebuild any model without re-pulling APIs, and transformation logic in dbt is testable and reviewable. We use ETL where compliance requires filtering or masking before the data ever lands.
Bounded by the source, not by us. Most ad platform reporting APIs update hourly at best and restate for days, so hourly ingestion is genuinely current. Event streams like server-side conversions can run in near real time. We are direct about which of your sources can support a live number and which cannot.
Every task is idempotent and writes atomically: loads go to a staging location and swap in only on success, so a retry produces the same result as a clean first run. Partial writes never become visible to reporting.
That is the goal. Transformations live in dbt as version-controlled SQL, which most analysts can already read and extend. We hand over the repo, the tests, the runbooks, and a working local setup.